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Published: 4 June 2021
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1

Ullah, Noor. "ANFIS BASED MODELS FOR ACCESSING QUALITY OF WIKIPEDIA ARTICLES." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4909.

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Wikipedia is a free, web-based, collaborative, multilingual encyclopedia project supported by the non-profit Wikimedia Foundation. Due to the free nature of Wikipedia and allowing open access to everyone to edit articles the quality of articles may be affected. As all people don’t have equal level of knowledge and also different people have different opinions about a topic so there may be difference between the contributions made by different authors. To overcome this situation it is very important to classify the articles so that the articles of good quality can be separated from the poor quality articles and should be removed from the database. The aim of this study is to classify the articles of Wikipedia into two classes class 0 (poor quality) and class 1(good quality) using the Adaptive Neuro Fuzzy Inference System (ANFIS) and data mining techniques. Two ANFIS are built using the Fuzzy Logic Toolbox [1] available in Matlab. The first ANFIS is based on the rules obtained from J48 classifier in WEKA while the other one was built by using the expert’s knowledge. The data used for this research work contains 226 article’s records taken from the German version of Wikipedia. The dataset consists of 19 inputs and one output. The data was preprocessed to remove any similar attributes. The input variables are related to the editors, contributors, length of articles and the lifecycle of articles. In the end analysis of different methods implemented in this research is made to analyze the performance of each classification method used.
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2

Jain, Aakanksha. "Application of Artificial Intelligence Techniques in the Prediction of Industrial Outfall Discharges." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39812.

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Artificial intelligence techniques have been widely used for prediction in various areas of sciences and engineering. In the thesis, applications of AI techniques are studied to predict the dilution of industrial outfall discharges. The discharge of industrial effluents from the outfall systems is broadly divided into two categories on the basis of density. The effluent with density higher than the water receiving will sink and called as negatively buoyant jet. The effluent with density lower than the receiving water will rise and called as positively buoyant jet. The effluent discharge in the water body creates major environmental threats. In this work, negatively buoyant jet is considered. For the study, ANFIS model is taken into consideration and incorporated with algorithms such as GA, PSO and FFA to determine the suitable model for the discharge prediction. The training and test dataset for the ANFIS-type models are obtained by simulating the jet using the realizable k-ε turbulence model over a wide range of Froude numbers i.e. from 5 to 60 and discharge angles from 20 to 72.5 degrees employing OpenFOAM platform. Froude number and angles are taken as input parameters for the ANFIS-type models. The output parameters were peak salinity (Sm), return salinity (Sr), return point in x direction (xr) and peak salinity coordinates in x and y directions (xm and ym). Multivariate regression analysis has also been done to verify the linearity of the data using the same input and output parameters. To evaluate the performance of ANFIS, ANFIS-GA, ANFIS-PSO, ANFIS-FFA and multivariate regression model, some statistical parameters such as coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and average absolute deviation in percentage are determined. It has been observed that ANFIS-PSO is better in predicting the discharge characteristics.
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3

Chakraborty, Joyraj, and Venkata Krishna chaithanya varma Jampana. "ANFIS BASED OPPURTUNISTIC POWER CONTROL FOR COGNITIVE RADIO IN SPECTRUM SHARING." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5042.

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Cognitive radio is a intelligent technology that helps in resolving the issue of spectrum scarcity. In a spectrum sharing network, where secondary user can communicate simultaneously along with the primary user in the same frequency band, one of the challenges in cognitive radio is to obtain balance between two conflicting goals that are to minimize the interference to the primary users and to improve the performance of the secondary user. In our thesis we have considered a primary link and a secondary link (cognitive link) in a fading channel. To improve the performance of the secondary user by maintaining the Quality of Service (Qos) to the primary user, we considered varying the transmit power of the cognitive user. Efficient utilization of power in any system helps in improving the performance of that system. For this we proposed ANFIS based opportunistic power control strategy with primary user’s SNR and primary user’s channel gain interference as inputs. By using fuzzy inference system, Qos of primary user is adhered and there is no need of complex feedback channel from primary receiver. The simulation results of the proposed strategy shows better performance than the one without power control. Initially we have considered propagation environment without path loss and then extended our concept to the propagation environment with path loss where we have considered relative distance between the links as one of the input parameters.
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4

Зінченко, Руслан Миколайович, Руслан Николаевич Зинченко, Ruslan Mykolaiovych Zinchenko, Анна Вадимівна Гонщик, Анна Вадимовна Гонщик, Anna Vadymivna Honshchyk, and Д. Г. Кулагин. "Исследование возможности применения ANFIS-сети в системах диагностики состояния режущего инструмента." Thesis, Сумский государственный университет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/31225.

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Обработка материалов резанием все еще охватывает значительную долю всех операций производственного процесса. Одной из наиболее важных задач в исследованиях, затрагивающих область резания, является разработка методики, которая смогла бы обеспечить: оптимальное использование ресурса станка, рост производительности, повышение точности обработки, сокращение времени на простой станка и уменьшение затрат на режущий инструмент (РИ). При цитировании документа, используйте ссылку http://essuir.sumdu.edu.ua/handle/123456789/31225
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5

Hamdan, Hazlina. "An exploration of the adaptive neuro-fuzzy inference system (ANFIS) in modelling survival." Thesis, University of Nottingham, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.594875.

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Medical prognosis is the prediction of the future course and outcome of a disease and an indication of the likelihood of recovery from that disease. Prognosis is important because it is used to guide the type and intensity of the medication administered to patients. Patients are usually concerned with how long they will survive after diagnosis. Survival analysis describes the analysis of data that corresponds to the time from when an individual enters a study until the occurrence of some particular event or end-point. It is concerned with the comparison of survival curves for different combinations of risk factors. Analytical methods that are transparent for the clinician's understanding and explain individual inferences need to be considered when dealing with medical data. This thesis describes a methodology for modelling survival by utilising the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS). A hybrid intelligent system which combines the fuzzy logic qualitative approach and adaptive neural network capabilities towards better performance. The ANFIS approach was applied in modelling survival of breast cancer based on patient groups derived from the Nottingham Prognostic Index (NPI). A comparison of the proposed method with the existing methods in the capability to predict the survival rate is presented. The use of a fuzzy inference system (FIS) in modelling survival is expected to offer the capability to deliver the process of turning data into knowledge that can be understood by people. The design of rules can be performed either by human experts or using appropriate approaches to build high quality PIS to represent the knowledge. In this thesis, represent an automatic generation of membership functions and rules from the data. Further, corresponding subsequent adjustments have been made to the model to give towards more satisfactory performance. The final premise and consequent parameters obtained are then used to predict the survival for each time interval. A framework for modelling survival with the application of fuzzy inference system and back-propagation neural network was developed and is described in this thesis. In this framework, a different way of partitioning the input space can be selected to define the membership functions for examples using expert knowledge, equaliser partitioning, fuzzy c-means or subtractive clustering techniques. Further, the rule base can be established by enumerating all possible combinations of membership functions of all inputs. After the initialisation of the fuzzy inference structure, the replication data (until time to event) will be subject to training using the gradient descent and nonnegative least square algorithm to estimate the conditional event probability. This framework is validated over a synthetic dataset and a novel dataset of patients following operative surgery of ovarian cancer. The proposed framework can be applied to estimate the hazard and survival curve between different prognostic factors and survival time with the explanation capabilities.
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6

Martins, Jos? Kleiton Ewerton da Costa. "An?lise de diferentes t?cnicas de controle na estrutura do ANFIS modificado." PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA EL?TRICA E DE COMPUTA??O, 2017. https://repositorio.ufrn.br/jspui/handle/123456789/24224.

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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)
O trabalho faz uma an?lise de diferentes t?cnicas de controle na estrutura do ANFIS modificado, m?todo recente que se originou a partir de uma altera??o na estrutura do ANFIS, para realizar identifica??o e controle de plantas com ampla faixa de opera??o e n?o linearidade acentuada. O ANFIS modificado ? dividido em dois grandes est?gios, o primeiro sendo a identifica??o e o segundo o controle. Para realizar a identifica??o pode-se utilizar quaisquer t?cnicas. Nesse trabalho foram exploradas as t?cnicas de identifica??o de sistemas lineares mais conhecidas na literatura e o m?todo dos m?nimos quadrados. Assim como no est?gio da identifica??o, o est?gio de controle tamb?m permite utilizar quaisquer t?cnicas de projeto. Nesse trabalho foram exploradas as t?cnicas de sintonia de controladores PID mais conhecidas na literatura, na qual os controladores projetados foram incorporados na estrutura do ANFIS modificado para a obten??o de um controlador global n?o linear. Foi escolhido um sistema de tanques com multisse??es como estudo de caso e assim foi realizada a sua identifica??o atrav?s do ANFIS modificado, mostrando as qualidades do m?todo. Em seguida foi realizada uma compara??o de desempenho do ANFIS modificado utilizando os diferentes m?todos de sintonia e ao final chegando a uma metodologia sistem?tica para utiliza??o do ANFIS modificado como controlador global.
This work makes an analysis of different control techniques in the modified ANFIS structure, this method is recent and originated from a change in the ANFIS structure for perform identification and control of plants with wide operating range and accentuated non-linearity. The modified ANFIS is divided into two major stages, the first is the identification and the second is the control. In order to perform the identification, it is possible to use any techniques. In this work was explored the linear system identification more known in the literature and the least square estimation. As in the identification stage, the control stage can also use any techniques. This work the tuning of PID controllers will be explored, in which the designed controllers will be incorporated into the modified ANFIS structure to obtain a non-linear controller. A system of tanks with multisections was chosen as a case study and its identification through the modified ANFIS was performed, showing the qualities of the method. Then a performance comparison of the modified ANFIS will be performed using the different tuning methods and show a systematic methodology for use the modified ANFIS as global controller.
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7

Aldobhani, Abdulaziz Mohamed Saeed. "Maximum power point tracking of PV system using ANFIS prediction and fuzzy logic tracking." Thesis, De Montfort University, 2008. http://hdl.handle.net/2086/4284.

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Operating faraway from maximum power point decreases the generated power from photovoltaic (PV) system. For optimum operation, it is necessary to continually track the maximum power point of the PV solar array. However with huge changes in external influences and the nonlinear relationship of electrical characteristics of PV panels it is a difficult problem to identify the maximum power point as a function of these influences. Many tracking control strategies have been proposed to track maximum power point such as perturb and observe, incremental conductance, parasitic capacitance, and neural networks. These proposed methods have some disadvantages such as high cost, difficulty, complexity and nonstability. This thesis presents a novel approach based on Adaptive NeuroFuzzy Inference System (ANFIS) to predict the maximum power point utilising the actual field data, which is performed in different environmental conditions. The short circuit current and open circuit voltage are used as inputs to PV panels instead of solar irradiation and cell junction temperature. The predicted $V_{max}$from ANFIS model is used as a reference voltage for fuzzy logic controller (FLC). The FLC is used to adjust the duty cycle of the electronic switch of two types of DC-DC converter. These DC-DC converters are used to interface between the load voltage and PV panels. The duty cycle of the electronic switch of the DC-DC converter is adjusted until the input voltage of the converter tracks the predicted $V_{max}$of the PV system. FLC rules and membership functions are designed to achieve the most promising performance at different environmental conditions, different load types and different rate of changes in the duty cycle of Buck-Boost and Buck converters. The membership functions and fuzzy rules of FLC are designed to balance between different required features such as quick tracking under different environmental conditions, high accuracy, stability and high efficiency.
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8

SILVA, Geane Bezerra da. "Sistema híbrido de previsão de carga elétrica em curto prazo utilizando redes neurais artificiais e lógica fuzzy." Universidade Federal de Pernambuco, 2006. https://repositorio.ufpe.br/handle/123456789/5485.

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O presente trabalho apresenta um sistema de previsão de carga horária em curto prazo (sete dias à frente) formado por duas etapas. Na primeira etapa foram escolhidas duas redes neurais artificiais para prever o consumo diário total em um horizonte de sete dias à frente, uma rede para os dias úteis e outra para aos dias não-úteis, o processo de escolha das redes passou por uma análise da estrutura de entrada, da base de dados e do algoritmo de treinamento. Para gerar as melhores redes utilizou-se o método k-fold crossvalidation. A segunda etapa é responsável em fornecer o comportamento da curva de carga, ou seja, a distribuição horária do consumo diário, para isso utilizou-se o sistema ANFIS (Adaptive Network-based Fuzzy Inference System) para gerar um Sistema de Inferência Fuzzy- SIF que fornece um coeficiente que representa a fração do consumo horário em relação ao consumo diário, para inicialização dos modelos optou-se pela comparação entre dois métodos: o método de clusterização subtrativa desenvolvido por Chui S e o método por inspeção onde o SIF é gerado a partir do conhecimento do especialista. Optou-se por estes modelos devido à facilidade de implementação, a capacidade de generalização e resposta rápida. Os resultados obtidos foram comparados com a bibliografia e mostram que o modelo desenvolvido tem alta capacidade de generalização e apresenta baixos valores de MAPE (erro médio percentual), além de utilizar somente dados de carga elétrica como entrada para as redes e para o sistema ANFIS sem a necessidade de dados climáticos
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9

Al-Dunainawi, Yousif Khalaf Yousif. "Intelligent Control for distillation columns." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15597.

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Nowadays, industrial processes are having to be rapidly developed to meet high standards regarding increases in the production rate and/or improving product quality. Fulfilling these requirements is having to work in tandem with the pressure to reduce energy consumption due to global environmental regulations. Consequently, most industrial processes critically rely on automatic control, which can provide efficient solutions to meet such challenges and prerequisites. For this thesis, an intelligent system design has been investigated for controlling the distillation process, which is characterised by highly nonlinear and dynamic behaviour. These features raise very challenging tasks for control systems designers. Fuzzy logic and artificial neural networks (ANNs) are the main methods used in this study to design different controllers, namely: PI- PD- and PID-like fuzzy controllers, ANN-based NARMAL2 in addition to a conventional PID controller for comparison purposes. Genetic algorithm (GA) and particle swarm optimisation (PSO) have also been utilised to tune fuzzy controllers by finding the best set of scaling factors. Finally, an intelligent controller is proposed, called ANFIS-based NARMA-L2, which uses ANFIS as an approximation approach for identifying the underlying systems in a NARMA-L2 configuration. The controllers are applied to control two compositions of a binary distillation column, which has been modelled and simulated in MATLAB® and on the Simulink® platform. Comparative analysis has been undertaken to investigate the controllers' performance, which shows that PID-like FLC outperforms the other tested fuzzy control configurations, i.e. PI- and PD-like. Moreover, PSO has been found to outperform GA in finding the best set of scaling factors and over a shorter time period. Subsequently, the performance of PID-like FLC has been compared with ANN-based NARMA-L2 and the proposed ANFIS-based NARMA-L2, by subjecting the controlled column to different test scenarios. Furthermore, the stability and robustness of the controllers have been assessed by subjecting the controlled column to inputs variance and disturbances situations. The proposed ANFIS-based NARMAL2 controller outperforms and demonstrates more tolerance of disturbances than the other controllers. Finally, the study has involved investigating the control of a multicomponent distillation column due to its significant enhancement in operational efficiency regarding energy saving and recent widespread implementation. That is, Kaibel's distillation column with 4×4 configuration has been simulated also in MATLAB® and on the Simulink® platform with the proposed controller being implemented to control the temperatures of the column and the outcomes subsequently compared with conventional PID controllers. Again, the novel controller has proven its superiority regarding the disturbances tolerance as well as dealing with the high dynamics and nonlinear behaviour.
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10

Jasmine, Mansura. "A Comparative Study on Prediction of Evaporation in Arid Area Based on Artificial Intelligence Techniques." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40313.

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Estimation of evaporation from open water is essential for hydrodynamics, manufacturing industries, irrigation, farming, environmental protection and many other purposes. It is also important for proper management of hydrological resources such as reservoirs, lakes and rivers. Recent methods are mostly data-driven methods, such as using Artificial Intelligence techniques. Adaptive Neuro Fuzzy Inference System (ANFIS) is one of them and has been widely adopted in many hydrological fields for its simplicity. The current research presents a comparative study on the impact of optimization techniques such as Firefly Algorithm (FFA), Genetic Algorithm (GA), Particle Swarm Optimizer (PSO) and Ant Colony Optimization (ACO) on obtained results. In addition, a practical method named Multi Gene-genetic Programming (MGGP) is employed to propose an equation for the estimation of the Evaporation. Six different measured weather variables are taken, which are maximum, minimum and average air temperature, sunshine hours, wind speed and relative humidity. Models are separately calibrated with total data set collected over an eight-year period of 2010-2017 at the specified station “Arizona” in the United States of America. Ten statistical indices are calculated to verify the results. All optimizers were observed and compared to check if the results are better than ANFIS or not. The objectives of the adoption of different optimizer techniques was to verify the accuracy of the prediction by ANFIS model. Comparisons showed that ANFIS and MGGP are slightly better than the other models. MGGP model is different from other models in a way that it provides a set of equations instead of showing numerical values; therefore, the computational time is high. PSO, FFA, ACO and GA are considered as optimizers in the main model. Though PSO provided very similar results to the ANFIS model and MGGP gives even better results than basic ANFIS model. ANFIS is easier in terms of model formation. ANFIS is simpler to build and easy to operate. Since the prediction was quite identical in all cases, the ANFIS model was suggested due to its simplicity.
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11

Abdulshahed, Ali. "The application of ANN and ANFIS prediction models for thermal error compensation on CNC machine tools." Thesis, University of Huddersfield, 2015. http://eprints.hud.ac.uk/id/eprint/27946/.

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Thermal errors can have significant effects on Computer Numerical Control (CNC) machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This thesis first reviews different methods of designing thermal error models, before concentrating on employing Artificial Intelligence (AI) methods to design different thermal prediction models. In this research work the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as the backbone for thermal error modelling. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this thesis, temperature measurement was supplemented by direct distortion measurement at accessible locations. The location of temperature measurement must also provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this thesis, a new intelligent system for reducing thermal errors of machine tools using data obtained from thermography data is introduced. Different groups of key temperature points on a machine can be identified from thermal images using a novel schema based on a Grey system theory and Fuzzy C-Means (FCM) clustering method. This novel method simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. An Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means clustering (ANFIS-FCM) is then employed to design the thermal prediction model. In order to optimise the approach, a parametric study is carried out by changing the number of inputs and number of Membership Functions (MFs) to the ANFIS-FCM model, and comparing the relative robustness of the designs. The proposed approach has been validated on three different machine tools under different operation conditions. Thus the proposed system has been shown to be robust to different internal heat sources, ambient changes and is easily extensible to other CNC machine tools. Finally, the proposed method is shown to compare favourably against alternative approaches such as an Artificial Neural Network (ANN) model and different Grey models.
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Chen, Chao. "A novel framework for the implementation and evaluation of type-1 and interval type-2 ANFIS." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/49442/.

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This thesis explores a novel framework for implementing and evaluating type-1 (T1) and interval type-2 (IT2) models of Adaptive Network Fuzzy Inference Systems (ANFIS). A fundamental requirement for this research is the capability to reliably and efficiently implement ANFIS models. In the last ten years, many studies have been devoted to creating IT2 ANFIS models. However, a clear architecture for IT2 ANFIS has not yet been presented. This somehow has been an obstacle to the research of IT2 ANFIS and its application to real-world problems. In this thesis, we introduce an extended ANFIS architecture that can be used for both T1 and IT2 models. In conjunction with this, a crucial obstacle to the use of IT2 fuzzy systems in general (and including IT2 ANFIS) is that IT2 models are often more computationally expensive than T1 models. Note that a bottle-neck for IT2 ANFIS is to aggregate the output of each rule produced by the inference process of the Karnik-Mendel (KM) algorithm. Many enhanced algorithms have been proposed to improve the computational efficiency of the KM algorithm. However, all of these algorithms are still based on iterative procedures to determine the switch points required for the lower and upper bounds of defuzzification. This thesis introduces a `direct approach' which can be used to determine these switch points based on derivatives, without the need for multiple iterations. When comparing various models (including T1 and IT2 ANFIS models), it is necessary to conduct fair comparisons. Partly to address this issue, a new accuracy measure is proposed which combines the best features of various alternative measures without having their common drawbacks. Experimental comparisons are made between T1 and IT2 ANFIS using the novel accuracy measure in addition to the commonly used RMSE, on both synthetic and real-world data. Finally, it is shown that IT2 ANFIS models are not easy to optimise from scratch due to difficulties with the output intervals, that are not present in T1 ANFIS models. Detailed experiments are carried out to evaluate the comparative performance of IT2 ANFIS models, including the best method for initialising the IT2 membership functions. In summary, a coherent framework for efficiently implementing IT2 ANFIS models and fairly evaluating their comparative performance is presented. This framework allows the implementation of IT2 ANFIS in any application context, and the resultant performance to be carefully considered, since clear performance improvement compared to T1 ANFIS may not always be found.
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XAVIER, Priscila Branquinho. "Análise e Comparação de Modelos de Previsão de Vazões para o Planejamento Energético, Utilizando Séries Temporais." Universidade Federal de Goiás, 2009. http://repositorio.bc.ufg.br/tede/handle/tde/994.

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n the planning of the energetic operation, analysis and forecasts of the flow are very important. A huge difficulty in the forecast of flow is the seasonality presence, due to drought and flood periods in the year. Many scientists, with different methodologies, have been concerned with finding a best model, compared with the utilized by Brazil s system - Markovian Model. The Makovian Model, or selfregressive with order 1, is a Box & Jenkins methodology, and requires data handling to treat non-stationarity, or the use of regular models, requiring a hardly theoretical formulation for the statistical procedures. Therefore, the statistical models, autoregressive model with seasonality and Holt-Winters model, of treatment of temporal series are presented and, carried out the flow s analysis and forecast for three study groups, in two different (historical) horizons. The performance of the models was compared and the results showed that the proposed models presents better adjust than the model adopted by Brazilian system
No planejamento da operação energética, a análise e previsão de vazões são muito importantes. Uma grande dificuldade na previsão de vazões é a presença da sazonalidade, devido aos períodos de seca e cheia no ano. Muitos estudiosos, com metodologias diversas, têm se preocupado em encontrar um modelo de melhor ajuste, em comparação ao utilizado pelo sistema brasileiro, ou seja, o modelo auto-regressivo de ordem 1, que consiste numa metodologia de Box & Jenkins e exige manuseio nos dados para tratar a não-estacionariedade. O presente trabalho analisa e compara os modelo utilizados pelo sistema brasileiro (PAR), com modelo matemático que considera a sazonalidade dos dados (SAR) e o método de Holt-Winters e, modelos amplamente estudados como PARMA e ANFIS. O desempenho dos modelos foi comparado e os resultados mostraram que em muitos estudos os modelos PAR/PARMA e ANFIS apresentam melhor ajuste , no geral, em relação aos demais
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Lima, Fábio. "Estimador neuro-fuzzy de velocidade aplicado ao controle vetorial sem sensores de motores de indução trifásicos." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-20092011-150232/.

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Este trabalho apresenta uma alternativa ao controle vetorial de motores de indução, sem a utilização de sensores para realimentação da velocidade mecânica do motor. Ao longo do tempo, diversas técnicas de controle vetorial têm sido propostas na literatura. Dentre elas está a técnica de controle por orientação de campo (FOC), muito utilizada na indústria e presente também neste trabalho. A principal desvantagem do FOC é a sua grande sensibilidade às variações paramétricas da máquina, as quais podem invalidar o modelo e as ações de controle. Nesse sentido, uma estimativa correta dos parâmetros da máquina, torna-se fundamental para o acionamento. Este trabalho propõe o desenvolvimento e implementação de um estimador baseado em um sistema de inferência neuro-fuzzy adaptativo (ANFIS) para o controle de velocidade do motor de indução trifásico em um acionamento sem sensores. Pelo fato do acionamento em malha fechada admitir diversas velocidades de regime estacionário para o motor, uma nova metodologia de treinamento por partição de frequência é proposta. Ainda, faz-se a validação do sistema utilizando a orientação de campo magnético no referencial de campo de entreferro da máquina. Simulações para avaliação do desempenho do estimador mediante o acionamento vetorial do motor foram realizadas utilizando o programa Matlab/Simulink. Para a validação prática do modelo, uma bancada de testes foi implementada; o acionamento do motor foi realizado por um inversor de frequência do tipo fonte de tensão (VSI) e o controle vetorial, incluindo o estimador neuro-fuzzy, foi realizado pelo pacote de tempo real do programa Matlab/Simulink, juntamente com uma placa de aquisição de dados da National Instruments.
This work presents an alternative sensorless vector control of induction motors. Several techniques for induction motor control have been proposed in the literature. Among these is the field oriented control (FOC), strongly used in industries and also in this work. The main drawback of the FOC technique is its sensibility to deviations of the parameters of the machine, which can deteriorate the control actions. Therefore, an accurate determination of the machines parameters is mandatory to the drive system. This work proposes the development of an adaptive neuro-fuzzy inference system (ANFIS) estimator to control the angular speed of a three-phase induction motor in a sensorless drive. In a closed loop configuration, several speed commands can be imposed to the motor. Thus, a new frequency partition training of ANFIS is proposed. Moreover, the ANFIS speed estimator is validated in a magnetizing flux oriented control scheme. Simulations to evaluate the performance of the estimator considering the vector drive system were done by the Matlab/Simulink. To determine the benefits of the proposed model a practical system was implemented using a voltage source inverter (VSI) and the vector control including the ANFIS estimator, carried out by the Real Time Toolbox from Matlab/Simulink and a data acquisition card from National Instruments.
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15

Block, Saldaña Henry José. "Diseño de una arquitectura para un sistema neurodifuso ANFIS sobre un FPGA aplicado a la generación de funciones." Bachelor's thesis, Pontificia Universidad Católica del Perú, 2010. http://tesis.pucp.edu.pe/repositorio/handle/123456789/515.

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En la presente tesis, se realizó el diseño de una arquitectura para un sistema neurodifuso ANFIS. Se tomó en consideración un sistema de orden cero de dos entradas y una salida, que cuenta con funciones de pertenencia triangulares en los antecedentes de las reglas difusas. Además, se tuvo en cuenta que el entrenamiento del sistema es realizado fuera de línea (off-line), en MATLAB. La arquitectura diseñada se dividió en cuatro bloques: Fuzzificador, Permutador, Inferencia y Defuzzificador. Cada uno de estos bloques fue tratado como un subsistema y descrito por separado para facilitar su diseño. Posteriormente, se procedió a juntar los cuatro bloques, dando como resultado la arquitectura propuesta para el sistema neurodifuso ANFIS. Esta arquitectura fue descrita de manera modular y genérica mediante el lenguaje de descripción de hardware VHDL y fue implementada en los FPGA Spartan-3 XC3S200 de la empresa Xilinx y Cyclone II EP2C35 de la empresa Altera, utilizando las herramientas que se encuentran dentro de los entornos de desarrollo ISE 11 y Quartus II 9.1, respectivamente. El sistema diseñado fue aplicado a la generación de funciones. Primero, se eligió una función no lineal y se llevó a cabo el entrenamiento del sistema en MATLAB para obtener los parámetros de los antecedentes y consecuentes de las reglas difusas. Después, estos parámetros fueron convertidos a una representación binaria en punto-fijo complemento a dos y almacenados en las memorias ROM del código en VHDL. Finalmente, se realizaron simulaciones sobre los dos FPGA, mencionados anteriormente, para verificar la operación del sistema y poder evaluar su desempeño. Entre los resultados obtenidos, destaca que el tiempo requerido por el sistema para calcular un valor de la función es menor a 10 s (trabajando a una frecuencia de reloj de 50 MHz). Este valor es mucho menor al tiempo requerido por la aplicación en MATLAB, el cual fue de alrededor de 100 ms.
Tesis
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16

Fonseca, Carlos Andr? Guerra. "Estrutura ANFIS modificada para identifica??o e controle de plantas com ampla faixa de opera??o e n?o linearidade acentuada." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15222.

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In this work a modification on ANFIS (Adaptive Network Based Fuzzy Inference System) structure is proposed to find a systematic method for nonlinear plants, with large operational range, identification and control, using linear local systems: models and controllers. This method is based on multiple model approach. This way, linear local models are obtained and then those models are combined by the proposed neurofuzzy structure. A metric that allows a satisfactory combination of those models is obtained after the structure training. It results on plant s global identification. A controller is projected for each local model. The global control is obtained by mixing local controllers signals. This is done by the modified ANFIS. The modification on ANFIS architecture allows the two neurofuzzy structures knowledge sharing. So the same metric obtained to combine models can be used to combine controllers. Two cases study are used to validate the new ANFIS structure. The knowledge sharing is evaluated in the second case study. It shows that just one modified ANFIS structure is necessary to combine linear models to identify, a nonlinear plant, and combine linear controllers to control this plant. The proposed method allows the usage of any identification and control techniques for local models and local controllers obtaining. It also reduces the complexity of ANFIS usage for identification and control. This work has prioritized simpler techniques for the identification and control systems to simplify the use of the method
Neste trabalho prop?e-se uma modifica??o na estrutura neurofuzzy ANFIS (Adaptive Network Based Fuzzy Inference System) para a obten??o de um m?todo sistem?tico para identifica??o e controle de plantas com ampla faixa de opera??o e n?o linearidade acentuada, a partir de t?cnicas lineares de identifica??o e controle. Este m?todo se baseia na metodologia de m?ltiplos modelos. Dessa forma, obt?m-se modelos lineares locais e esses s?o combinados pela estrutura neurofuzzy proposta. Uma m?trica que permite combinar adequadamente esses modelos ? obtida ap?s o treinamento dessa estrutura, resultando na identifica??o global da planta. Para cada um desses modelos ? projetado um controlador. O controle global ? obtido a partir da combina??o dos sinais dos controladores locais. Essa mistura ? feita pelo ANFIS modificado. A modifica??o na arquitetura do ANFIS permite o compartilhamento do conhecimento adquirido pelo treinamento da estrutura empregada na combina??o de modelos locais. Assim n?o se faz necess?rio o treinamento da estrutura empregada na mistura de controladores. Avaliaram-se as estruturas modificadas atrav?s de dois estudos de caso. Verificou-se que ? poss?vel treinar apenas um ANFIS, para a obten??o de uma m?trica que permita a combina??o adequada dos modelos lineares, v?lidos localmente, e essa estrutura, j? ajustada, pode ser aplicada na combina??o de controladores lineares, projetados para cada um dos modelos, resultando em um sistema de controle que satisfaz as especifica??es de desempenho previamente estabelecidas. O m?todo proposto possibilita a utiliza??o de quaisquer t?cnicas de identifica??o e controle para a obten??o dos modelos e controladores locais, e a redu??o da complexidade de utiliza??o do ANFIS para identifica??o e controle. Neste trabalho priorizaram-se as t?cnicas mais simples de identifica??o e controle de sistemas de forma a simplificar a utiliza??o do m?todo
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17

Aslan, Muhittin. "Modeling The Water Quality Of Lake Eymir Using Artificial Neural Networks (ann) And Adaptive Neuro Fuzzy Inference System (anfis)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610211/index.pdf.

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Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate, Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: to develop models with ANN to predict DO concentration in Lake Eymir with high fidelity to actual DO data, to compare the success (prediction capacity) of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. For modeling studies &ldquo
Matlab R 2007b&rdquo
software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.
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18

Rodrigues, Marconi C?mara. "T?cnicas inteligentes h?dridas para o controle de sistemas n?o lineares." Universidade Federal do Rio Grande do Norte, 2006. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15349.

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A neuro-fuzzy system consists of two or more control techniques in only one structure. The main characteristic of this structure is joining one or more good aspects from each technique to make a hybrid controller. This controller can be based in Fuzzy systems, artificial Neural Networks, Genetics Algorithms or rein forced learning techniques. Neuro-fuzzy systems have been shown as a promising technique in industrial applications. Two models of neuro-fuzzy systems were developed, an ANFIS model and a NEFCON model. Both models were applied to control a ball and beam system and they had their results and needed changes commented. Choose of inputs to controllers and the algorithms used to learning, among other information about the hybrid systems, were commented. The results show the changes in structure after learning and the conditions to use each one controller based on theirs characteristics
Neste trabalho ? mostrado tanto o desenvolvimento quanto as caracter?sticas de algumas das principais t?cnicas utilizadas para o controle inteligente de sistemas. Partindo de um controlador fuzzy foi poss?vel aplicar t?cnicas de aprendizagem, similares ?s utilizadas pelas Redes Neurais Artificiais (RNA's), evoluir para os modelos neuro-fuzzy ANFIS e NEFCON. Estes modelos neuro-fuzzy foram aplicados a uma planta real do tipo ball and beam e tiveram tanto suas adapta??es quanto seus resultados comentados. Para cada controlador desenvolvido s?o especificadas as vari?veis de entrada, os par?metros utilizados para a adapta??o das vari?veis e os algoritmos aplicados em cada um deles. J? os resultados est?o voltados para a obten??o de um comparativo entre a fase inicial e a final da evolu??o dos controladores neuro-fuzzy, assim como, a aplicabilidade de cada um deles de acordo com suas caracter?sticas intr?nsecas
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19

Pigatto, André Vieira. "Desenvolvimento de um protótipo de sistema inteligente para análise da técnica de pedalada apresentada por ciclistas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/181808.

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Este trabalho apresenta o desenvolvimento de um sistema inteligente para análise da técnica de pedalada aplicada por ciclistas. Para isso, desenvolveu-se um par de pedais de encaixe instrumentados, a partir dos quais é possível medir a componente de força normal aplicada nas partes frontal e posterior dos pedais. O modelo virtual da célula de carga experimental foi desenvolvido através da digitalização dos pedais de encaixe comerciais, utilizando-se um sistema comercial de escaneamento 3D com precisão declarada de 0,1mm. Cada pedal foi instrumentado com oito extensômetros de resistência elétrica (HBM 1-LY-13-1.5/350). Posteriormente os carregamentos máximos em cada eixo de medida de força foram estabelecidos utilizando-se uma plataforma de aquisição comercial específica para medida de deformação mecânica. Considerando-se os valores determinados, desenvolveu-se o circuito de condicionamento e realizaram-se os ensaios de deformação estática, obtendo-se as funções de transferência de saída de tensão elétrica em função do carregamento mecânico. O erro de linearidade máximo, considerando todos os canais, ficou abaixo de 0,75% e a máxima incerteza expandida (k=2) por canal, obtida através da aplicação do método clássico, foi de 1,55%. Em sequência, integrouse o sistema de pedais desenvolvido a dois outros sistemas, são eles: um par pedivelas experimentais instrumentados, capazes de medir as três componentes da força aplicada aos pedais e transmitidas aos pedivelas com um erro de linearidade abaixo de 0,6% e uma incerteza combinada inferior a 3,22%, e um sistema de cinemetria comercial, cuja precisão declarada pelo fabricante é de 1mm. Para possibilitar uma comparação quantitativa entre treinos ou ciclistas, implementou-se um sistema inteligente, baseado em redes Neuro-Fuzzy (ANFIS). A partir dos valores da potência média, do desvio padrão da potência e da assimetria bilateral média, obtidos ao longo de ensaios realizados sob protocolo desenvolvido especificamente para este trabalho, um score que representa o nível da técnica de pedalada apresentado pelo ciclista é determinado. Com intuito de testar o sistema, desenvolveu-se um projeto de experimentos com 2 fatores controláveis (sujeito e nível de frenagem de um rolo de treinamento), e realizou-se ensaios com oito ciclistas de características fisiológicas e níveis de preparos distintos. Através da análise estatística, constatou-se que das 23 variáveis de resposta consideradas ao longo do experimento, 23 são influenciadas significativamente pelo fator controlado sujeito e oito são influenciadas significativamente pelo fator controlado nível de frenagem magnética.
This report describes the development of an intelligent pedaling technique analysis system. To accomplish that, a pair of road bicycle pedals (SHIMANO R540) were instrumented to measure the forces that are applied to the front and back regions of the pedals. The virtual models of the pedals were developed based on a 3D scanned mesh developed with aid of a commercial 3D scanning system with a precision of 0.1mm. Each pedal was instrumented with eight electrical resistance strain-gages (HBM 1-LY-13-1.5/350). After that, the range of the mechanical deformation of each measurement channel was determined with aid of an industrial deformation acquisition system. The conditioning circuit was developed based on the mechanical deformation ranges previously determined and the static calibration experiment was performed to determine the voltage output transfer functions. The maximum linearity error determined per channel was 0,75% and the maximum expanded uncertainty (k=2), determined applying the classical methodology, was 1,55%. After that, the instrumented pedals developed were integrated with two complementary systems, which are: a pair of instrumented crank arm load cells which measure the components of the force applied to the bicycle pedal with a linearity error under 0.6% and an uncertainty of 3,22% and an Optitrack motion track system with a declared accuracy of 1mm. An intelligent pedaling technique analysis system was implemented through an Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the cyclist pedaling technique score based on three inputs: the average power applied to bicycle pedal, the average power standard deviation and the bilateral asymmetry index, all of them collected under an experimental protocol specifically designed for this application. To evaluate the behavior of the system developed a randomized block experiment design with two controlled factors was performed indoor with aid of an ergometer roll; 160 sprints were conducted with eight subjects of different training levels. From the data collected an ANOVA test was performed, which confirmed that all the 23 response variables vary significantly in function of the subject’s controlled factor and eight of them vary significantly in function of the magnetic braking level.
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20

Spacca, Jordy Luiz Cerminaro. "Usando o Sistema de Inferência Neuro Fuzzy - ANFIS para o cálculo da cinemática inversa de um manipulador de 5 DOF /." Ilha Solteira, 2019. http://hdl.handle.net/11449/183448.

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Orientador: Suely Cunha Amaro Mantovani
Resumo: No estudo dos manipuladores são utilizados os conceitos da cinemática direta e a inversa. No cálculo da cinemática direta tem-se a facilidade da notação de Denavit-Hartenberg, mas o desafio maior é a resolução da cinemática inversa, que se torna mais complexa conforme aumentam os graus de liberdade do manipulador, além de apresentar múltiplas soluções. As variáveis angulares obtidas pelas equações da cinemática inversa são utilizadas pelo controlador, para posicionar o órgão terminal do manipulador em um ponto específico de seu volume de trabalho. Na busca de alternativas para contornar estes problemas, neste trabalho utilizam-se os Modelos Adaptativos de Inferência Neuro-Fuzzy - ANFIS para a resolução da cinemática inversa, por meio de simulações, para obter o posicionamento de um manipulador robótico de 5 graus de liberdade, composto por sete servomotores controlados pela plataforma de desenvolvimento Intel® Galileo Gen 2, usado como caso de estudo. Nas simulações usamse ANFIS com uma arquitetura com três e quatro funções de pertinência de entrada, do tipo gaussiana. O desempenho da arquitetura da ANFIS implementada foi comparado com uma Rede Perceptron Multicamadas, demonstrando com os resultados favoráveis a ANFIS, a sua capacidade de aprender e resolver com baixo erro quadrático médio e com precisão, a cinemática inversa para o manipulador em estudo. Verifica-se também, que a performance das ANFIS melhora, quanto à precisão dos resultados, demonstrado pelo desvio médio d... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: In the study of manipulator’s, the concepts of direct and inverse kinematics are used. In the computation of forward kinematics, it has of the ease of Denavit-Hartenberg notation, but the biggest challenge is the resolution of the inverse kinematics, which becomes more complex as the manipulator's degrees of freedom increase, besides presenting multiple solutions. The angular variables obtained by the inverse kinematics equations are used by the controller to position the terminal organ of the manipulator at a specific point in its work volume. In the search for alternatives to overcome these problems, in this work, the Adaptive Neuro-Fuzzy Inference Models (ANFIS) are used to solve the inverse kinematics, by means of simulations, to obtain the positioning of a robot manipulator of 5 degrees of freedom, consisting of seven servomotors controlled by the Intel® Galileo Gen 2 development platform, used as a case's study . In the simulations ANFIS's architecture are used three and four Gaussian membership functions of input. The performance of the implemented ANFIS architecture was compared to a Multi-layered Perceptron Network, demonstrating with the favorable results the ANFIS, its ability to learn and solve with low mean square error and with precision, the inverse kinematics for the manipulator under study. It is also verified that the performance of the ANFIS improves, as regards the accuracy of the results in the training process, , demonstrated by the mean deviation of the... (Complete abstract click electronic access below)
Mestre
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21

Wangphanich, Pilada Mechanical &amp Manufacturing Engineering Faculty of Engineering UNSW. "A simulation model for quantifying and reducing the bullwhip effect." Publisher:University of New South Wales. Mechanical & Manufacturing Engineering, 2008. http://handle.unsw.edu.au/1959.4/43272.

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Over the past of decade, the bullwhip effect has increasingly become a popular topic for researchers and practitioners in the area of supply chain management since it negatively influences cost, inventory, reliability and other important business processes in supply chain agents. Although there are many remedies for the bullwhip effect summarised in existing literature, it still occurs in several industries. This is partly because it is difficult to apply the results from existing research which analyse the bullwhip effect mainly in a simple supply chain. In addition, several tools and methodologies developed are used for analysing the bullwhip effect in a simple supply chain with several constraints. Therefore, this research aims to develop a unique simulation approach based on system dynamics modelling and Adaptive Network Based Fuzzy Inference System (ANFIS) for quantifying and reducing the bullwhip effect in a multi-product, multi-stage supply chain. System dynamics modelling which is a powerful simulation approach for studying and managing complex feedback system was selected as a main tool in this research. In addition, ANFIS was implemented in system dynamics modelling in order to increase the reliability of a system dynamics model for modelling soft variables. The proposed model covers variables influencing the bullwhip effect which are the structure of supply chain network, supply chain contributions and supply chain performances. As a result, a two layer simulation with three generic models was developed. The flexibility of this proposed model is the ability to model various types of ordering policies which are basic inventory policies, Material requirement planning (MRP) system and Just in time (JIT) approach. Three actual manufacturing supply chains were used as case studies to validate and demonstrate the flexibility of the model developed in this research. This model satisfactorily quantifies the bullwhip effect and the bullwhip effect levels identified in these case studies are significantly decreased by using the proposed simulation model. The successful results indicate that the model can be a useful alternative tool for supply chain managers to quantify and reduce the bullwhip effect in multi-product, multi-stage supply chains.
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22

Cocheteux, Pierre. "Contribution à la maintenance proactive par la formalisation du processus de pronostic des performances de systèmes industriels." Electronic Thesis or Diss., Nancy 1, 2010. http://www.theses.fr/2010NAN10090.

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Les contraintes des marchés et les attentes de la société vis-à-vis des systèmes industriels en termes économique, sécuritaire, environnementaux requièrent de considérer les performances de ces derniers de façon globale sur l'ensemble de leur cycle de vie. Cela nécessite de mettre en synergie, par exemple avec des ingénieries couplées dès la conception, le système principal et ses systèmes contributeurs, et notamment celui de soutien avec son processus pivot de maintenance. Cette focalisation intégrative sur la maintenance a conduit à évoluer d'anciennes pratiques de maintenance vers de nouvelles plus proactives faisant émerger des stratégies prévisionnelles dont le processus clé est le pronostic. Cependant ce processus fait l'objet d'un réel manque de formalisation et les travaux existants restent principalement centrés sur les composants, sans prendre en compte les performances des systèmes. Ainsi notre contribution porte sur la proposition d'architectures génériques de pronostic système permettant d'obtenir les évolutions futures des dégradations/défaillances des composants et des performances de niveaux système/sous-systèmes/composants : soit directement par un pronostic adapté, soit par modélisation de la causalité dysfonctionnelle sous forme de relations logiques supportées par un réseau de neurones flou ANFIS. Une méthodologie est associée pour définir les indicateurs de dégradation et de performance, aboutissant à la réalisation des architectures. Enfin la faisabilité de cette approche est démontrée sur un système de déroulage/pressage de la plateforme TELMA
Today requirements and constraints on industrial systems about economic, safety, ecological points of view lead to consider their performances with a global view taking into account all the system lifecycle. Thus the design of the system-of-interest has to be connected as soon as possible with the enabling systems designs, and more particularly the logistic support based on the key process of maintenance. This new consideration about maintenance allowed to change practices from reactive to predictive ones with the emergence of the proactive maintenance built on the prognostic process. However this process still lacks of generic formalization and existing works focus mainly on component level without tackling system performances. Therefore our contribution is related to the modelling of generic architectures for the systems prognostic which assesses future evolution of degradation/failure components and system/subsystem/component level performances: either by prognosticating with an adapted model, or by modelling the dysfunctional causality with logical relations supported by a neuro-fuzzy tool ANFIS. A methodology is given to define indicators for degradations and performances and to build architecture. Finally, the feasibility of this approach is shown on the manufacturing TELMA platform
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23

Spadotto, Marcelo Montepulciano [UNESP]. "Lógica ANFIS aplicada na estimação da rugosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/87176.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
A necessidade de aplicação de novos equipamentos em ambientes cada vez mais agressivos demandou a busca por novos produtos capazes de suportar altas temperaturas, inertes às corroções químicas e com alta rigidez mecânica. O avanço tecnógico na produção de materiais cerâmicos tornou possível o emprego de processos de fabricação que antes eram somente empregados em metais. Dentre os processos de usinagem de cerâmicas avançadas, a retificação é o mais utilizado devido às maiores taxas de remoção diferentemente do brunimento e das limitações geométricas do processo de lapidação. A rugosidade é um do parâmetros de saída do processo de retificação que influi, dentre outros fatores, na qualidade do deslizamento entre estruturas, podendo gerar aquecimento. Além disso, o desgaste da ferramenta de corte gerado durante o processo está associado aos custos fixos e a problemas relacionados com o acabamento superficial bem como a danos estruturais. Essas duas variáveis, rugosidade e desgaste, são objetos de estudos de muitos pesquisadores. Entretanto, o controle automático tem sido uma difícil tarefa de ser realizada devido às variações de parâmetros ocorridas no processo. Dessa maneira, o presente trabalho tem por objetivo aplicar a lógica ANFIS (Adaptive Neuro-Fuzzy Inference System) na estimação da rogosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas. A ferramenta de corte aplicada para retificar os corpos-de-prova de alumina (96%) foi um rebolo diamantado. A partir do processamento digital dos sinais de emissão acústica e potência média de corte foram calculadas as estatísticas: média, desvio padrão, potência máxima, DPO e DPKS. As estatísticas foram aplicadas com entradas de duas redes ANFIS, uma estimando valores de rugosidade e outra estimando valores de desgaste...
The need for implementation of new equipaments in an increasingly agressive environmentl demanded a search for new products capable of withstanding high temperatures, inert to chemical corrosion and high mechanical stiffeness. Technological advances in the production of ceramic materials have become possible with the employment of manufacturing processes that previously were only employed in metals. Among the advanced ceramics machining processes, the grinding process is the most used, because of higher removal rates in constrast with the honing process and geometric limitations of lapping process. The surface reoughness is one of the output parameters of grinding process that affects, among other factors, the quality of sliding between structures that may generate heat. Moreover, the wear of the cutting tool generated during the process is associated with fixed costs and problems related to suface finishing as well as structural damages. These two variables, surface roughness and wear, have been studied by many researchers; however, the automatic control has been a difficult task to be carry out due to parameters variations occurring in the process. Hence, this work aims to apply logic ANFIS (Adaptive Neuro-Fuzzy Inference System) in the estimation of surface roughness and wear of the cutting tool in the tangential griding process of advanced ceramics. The cutting tools used to grind workpieces of alumina (96%) was a diamond grinding wheel. From the digital processing of acoustic emission and average cutting power signals some statistics were calculated: mean, standard deviation, maximum power, DPO and DPKS. The statistics were applied as inputs of two ANFIS networks estimating surface roughess and wear values. The results had demonstrated that the statistics associated with the ANFIS network can be used in the estimation of surface roughness and wear. However, the wear ANFIS network... (Complete abstract click electronic access below)
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24

Spadotto, Marcelo Montepulciano. "Lógica ANFIS aplicada na estimação da rugosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas /." Bauru : [s.n.], 2010. http://hdl.handle.net/11449/87176.

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Orientador: Paulo Roberto de Aguiar
Banca: Eraldo Jannone da Silva
Banca: Eduardo Carlos Bianchi
Resumo: A necessidade de aplicação de novos equipamentos em ambientes cada vez mais agressivos demandou a busca por novos produtos capazes de suportar altas temperaturas, inertes às corroções químicas e com alta rigidez mecânica. O avanço tecnógico na produção de materiais cerâmicos tornou possível o emprego de processos de fabricação que antes eram somente empregados em metais. Dentre os processos de usinagem de cerâmicas avançadas, a retificação é o mais utilizado devido às maiores taxas de remoção diferentemente do brunimento e das limitações geométricas do processo de lapidação. A rugosidade é um do parâmetros de saída do processo de retificação que influi, dentre outros fatores, na qualidade do deslizamento entre estruturas, podendo gerar aquecimento. Além disso, o desgaste da ferramenta de corte gerado durante o processo está associado aos custos fixos e a problemas relacionados com o acabamento superficial bem como a danos estruturais. Essas duas variáveis, rugosidade e desgaste, são objetos de estudos de muitos pesquisadores. Entretanto, o controle automático tem sido uma difícil tarefa de ser realizada devido às variações de parâmetros ocorridas no processo. Dessa maneira, o presente trabalho tem por objetivo aplicar a lógica ANFIS (Adaptive Neuro-Fuzzy Inference System) na estimação da rogosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas. A ferramenta de corte aplicada para retificar os corpos-de-prova de alumina (96%) foi um rebolo diamantado. A partir do processamento digital dos sinais de emissão acústica e potência média de corte foram calculadas as estatísticas: média, desvio padrão, potência máxima, DPO e DPKS. As estatísticas foram aplicadas com entradas de duas redes ANFIS, uma estimando valores de rugosidade e outra estimando valores de desgaste... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: The need for implementation of new equipaments in an increasingly agressive environmentl demanded a search for new products capable of withstanding high temperatures, inert to chemical corrosion and high mechanical stiffeness. Technological advances in the production of ceramic materials have become possible with the employment of manufacturing processes that previously were only employed in metals. Among the advanced ceramics machining processes, the grinding process is the most used, because of higher removal rates in constrast with the honing process and geometric limitations of lapping process. The surface reoughness is one of the output parameters of grinding process that affects, among other factors, the quality of sliding between structures that may generate heat. Moreover, the wear of the cutting tool generated during the process is associated with fixed costs and problems related to suface finishing as well as structural damages. These two variables, surface roughness and wear, have been studied by many researchers; however, the automatic control has been a difficult task to be carry out due to parameters variations occurring in the process. Hence, this work aims to apply logic ANFIS (Adaptive Neuro-Fuzzy Inference System) in the estimation of surface roughness and wear of the cutting tool in the tangential griding process of advanced ceramics. The cutting tools used to grind workpieces of alumina (96%) was a diamond grinding wheel. From the digital processing of acoustic emission and average cutting power signals some statistics were calculated: mean, standard deviation, maximum power, DPO and DPKS. The statistics were applied as inputs of two ANFIS networks estimating surface roughess and wear values. The results had demonstrated that the statistics associated with the ANFIS network can be used in the estimation of surface roughness and wear. However, the wear ANFIS network... (Complete abstract click electronic access below)
Mestre
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25

Rodrigues, Marconi C?mara. "Identifica??o fuzzy-multimodelos para sistemas n?o lineares." Universidade Federal do Rio Grande do Norte, 2010. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15143.

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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
This paper presents a new multi-model technique of dentification in ANFIS for nonlinear systems. In this technique, the structure used is of the fuzzy Takagi-Sugeno of which the consequences are local linear models that represent the system of different points of operation and the precursors are membership functions whose adjustments are realized by the learning phase of the neuro-fuzzy ANFIS technique. The models that represent the system at different points of the operation can be found with linearization techniques like, for example, the Least Squares method that is robust against sounds and of simple application. The fuzzy system is responsible for informing the proportion of each model that should be utilized, using the membership functions. The membership functions can be adjusted by ANFIS with the use of neural network algorithms, like the back propagation error type, in such a way that the models found for each area are correctly interpolated and define an action of each model for possible entries into the system. In multi-models, the definition of action of models is known as metrics and, since this paper is based on ANFIS, it shall be denominated in ANFIS metrics. This way, ANFIS metrics is utilized to interpolate various models, composing a system to be identified. Differing from the traditional ANFIS, the created technique necessarily represents the system in various well defined regions by unaltered models whose pondered activation as per the membership functions. The selection of regions for the application of the Least Squares method is realized manually from the graphic analysis of the system behavior or from the physical characteristics of the plant. This selection serves as a base to initiate the linear model defining technique and generating the initial configuration of the membership functions. The experiments are conducted in a teaching tank, with multiple sections, designed and created to show the characteristics of the technique. The results from this tank illustrate the performance reached by the technique in task of identifying, utilizing configurations of ANFIS, comparing the developed technique with various models of simple metrics and comparing with the NNARX technique, also adapted to identification
Este trabalho apresenta uma nova t?cnica de identifica??o multimodelos baseada em ANFIS para sistemas n?o lineares. Nesta t?cnica, a estrutura utilizada ? do tipo fuzzy Takagi-Sugeno cujos consequentes s?o modelos lineares locais que representam o sistema em diferentes pontos de opera??o e os antecedentes s?o fun??es de pertin?ncia cujos ajustes s?o realizados pela fase de aprendizagem da t?cnica neuro-fuzzy ANFIS. Modelos que representem o sistema em diferentes pontos de opera??o podem ser encontrados com t?cnicas de lineariza??o como, por exemplo, o m?todo dos M?nimos Quadrados que ? robusto a ru?dos e de simples aplica??o. Cabe ? fase de implica??o do sistema fuzzy informar a propor??o de cada modelo que deve ser empregada, utilizando, para isto, as fun??es de pertin?ncia. As fun??es de pertin?ncia podem ser ajustadas pelo ANFIS com o uso de algoritmos de redes neurais, como o de retropropaga??o do erro, de modo que os modelos encontrados para cada regi?o sejam devidamente interpolados e, assim, definam-se a atua??o de cada modelo para as poss?veis entradas do sistema. Em multimodelos a defini??o de atua??o de modelos ? conhecida por m?trica e, como neste trabalho ? realizada pelo ANFIS, ser? denominada de m?trica ANFIS. Desta forma, uma m?trica ANFIS ? utilizada para interpolar v?rios modelos, compondo o sistema a ser identificado. Diferentemente do ANFIS tradicional, a t?cnica desenvolvida necessariamente representa o sistema em v?rias regi?es bem definidas por modelos inalter?veis que, por sua vez, ter?o sua ativa??o ponderada a partir das fun??es de pertin?ncia. A sele??o de regi?es para a aplica??o do m?todo dos M?nimos Quadrados ? realizada manualmente a partir da an?lise gr?fica do comportamento do sistema ou a partir do conhecimento de caracter?sticas f?sicas da planta. Esta sele??o serve como base para iniciar a t?cnica definindo modelos lineares e gerando a configura??o inicial das fun??es de pertin?ncia. Experimentos s?o realizados em um tanque did?tico, com m?ltiplas se??es, projetado e desenvolvido com a finalidade de mostrar caracter?sticas da t?cnica. Os resultados neste tanque ilustram o bom desempenho alcan?ado pela t?cnica na tarefa de identifica??o, utilizando, para isto, v?rias configura??es do ANFIS, comparando a t?cnica desenvolvida com m?ltiplos modelos de m?trica simples e comparando com a t?cnica NNARX, tamb?m adaptada para identifica??o
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26

Guner, Evren. "Adaptive Neuro Fuzzy Inference System Applications In Chemical Processes." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1252246/index.pdf.

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Neuro-Fuzzy systems are the systems that neural networks (NN) are incorporated in fuzzy systems, which can use knowledge automatically by learning algorithms of NNs. They can be viewed as a mixture of local experts. Adaptive Neuro-Fuzzy inference system (ANFIS) is one of the examples of Neuro Fuzzy systems in which a fuzzy system is implemented in the framework of adaptive networks. ANFIS constructs an input-output mapping based both on human knowledge (in the form of fuzzy rules) and on generated input-output data pairs. Effective control for distillation systems, which are one of the important unit operations for chemical industries, can be easily designed with the known composition values. Online measurements of the compositions can be done using direct composition analyzers. However, online composition measurement is not feasible, since, these analyzers, like gas chromatographs, involve large measurement delays. As an alternative, compositions can be estimated from temperature measurements. Thus, an online estimator that utilizes temperature measurements can be used to infer the produced compositions. In this study, ANFIS estimators are designed to infer the top and bottom product compositions in a continuous distillation column and to infer the reflux drum compositions in a batch distillation column from the measurable tray temperatures. Designed estimator performances are further compared with the other types of estimators such as NN and Extended Kalman Filter (EKF). In this study, ANFIS performance is also investigated in the adaptive Neuro-Fuzzy control of a pH system. ANFIS is used in specialized learning algorithm as a controller. Simple ANFIS structure is designed and implemented in adaptive closed loop control scheme. The performance of ANFIS controller is also compared with that of NN for the case under study.
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27

Marques, Adriano de Souza [UNESP]. "Modelagem em superfícies inclinadas das radiações global e difusa usando técnicas de aprendizado de máquina." Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/154585.

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Neste trabalho é realizado um estudo para estimar a transmissividade da radiação global (Ktβh) e a fração difusa (Kdβh) incidentes em uma superfície com inclinação de 22,85° na base horária utilizando técnicas de aprendizado de máquina (TAM), a partir de dados obtidos no período de 1998 a 2001 em Botucatu/SP/Brasil. As estimativas foram realizadas usando uma série de combinações de variáveis astronômicas e geográficas por meio de três técnicas de redes neurais artificiais (RNA) do tipo Perceptron Multicamadas (MLP), Função de Base Radial (RBF) e Regressão Generalizada (GRNN) e do Sistema Adaptativo de Inferência Neuro Fuzzy (ANFIS). Como referência foram elaborados modelos estatísticos (ME) de regressão linear e polinomial. No Capítulo 1 as estimativas de (Ktβh) foram realizadas por combinações de variáveis medidas e calculadas a partir da irradiação global na superfície horizontal (HgH) e nas estimativas de (Kdβh) utilizou-se combinações de variáveis medidas e calculadas a partir de (HgH) e da irradiação global na superfície inclinada (Hgβ). No Capítulo 2 as estimativas de (Kdβh) foram realizadas por combinações de variáveis medidas e calculadas a partir das irradiações difusa (HdH) e global (HgH) obtidas na superfície horizontal. Os indicadores estatísticos r (correlação), RMSE(%) (precisão) e MBE(%) (exatidão) foram utilizados para avaliar os resultados das estimativas. No capítulo 1 os melhores resultados nas estimativas de (Ktβh) a partir das combinações realizadas com (HgH) foram: MLP - RMSE=3,73%; RBF - RMSE=3,99%; GRNN - RMSE=5,27%; ANFIS - RMSE=3,78% e ME - RMSE=6,65%. Nesse caso os indicadores de precisão mostram uma redução de aproximadamente 44% com o uso da técnica (MLP) em comparação ao modelo estatístico (ME). Nas estimativas de (Kdβh) a partir das combinações de (HgH) os melhores resultados foram: MLP - RMSE=21,69%; RBF - RMSE=25,43%; GRNN - RMSE=29,39%; ANFIS - RMSE=23,08% e - ME - RMSE=35,35%. Da mesma forma os indicadores de precisão mostram uma redução de aproximadamente 39% com o uso da técnica (MLP) em comparação ao modelo estatístico (ME). E nas estimativas de (Kdβh) a partir das combinações realizadas com (Hgβ) os melhores resultados foram: MLP - RMSE=20,32%; RBF - RMSE=21,95%; GRNN - RMSE=29,11%; ANFIS - RMSE=21,75% e ME - RMSE=36,48%. Os indicadores de precisão mostram uma redução de aproximadamente 44% com o uso da técnica (MLP) em comparação ao modelo estatístico (ME). No capítulo 2 as melhores estimativas de (Kdβh) a partir das combinações realizadas com (HdH) foram: MLP - RMSE=4,03%; RBF - RMSE=5,84%; GRNN - RMSE=10,85%; ANFIS - RMSE=4,15% e ME - RMSE=12,42%. Os indicadores de precisão mostram uma redução de aproximadamente 67% com o uso da técnica (MLP) em comparação ao modelo estatístico (ME). Nas estimativas de (Kdβh) a partir de (HgH) os melhores resultados foram: MLP - RMSE=21,69%; RBF - RMSE=25,43%; GRNN - RMSE=29,39%; ANFIS - RMSE=23,08% e ME - RMSE=35,35%. Os indicadores de precisão mostram uma redução de aproximadamente 39% com o uso da técnica (MLP) em comparação ao modelo estatístico (ME). Os resultados mostram que a técnica de rede neural artificial MLP apresentou os melhores índices em todas as estimativas de (Ktβh) e (Kdβh) com reduções significativas quando comparadas aos resultados obtidos com as estimativas obtidas com os modelos estatísticos. Pela análise dos resultados é possível observar que o uso das técnicas de aprendizado de máquina (TAM) nas combinações de variáveis propostas e com os dados obtidos de Botucatu/SP, se apresentam como alternativa aos modelos estatísticos (ME) para estimar as variáveis de (Ktβh) e (Kdβh).
In this work, a study was carried out to estimate the transmissivity of the global radiation (Ktβh) and the diffuse fraction (Kdβh) incident on a surface with slope of 22.85 ° in the hourly basis using machine learning techniques (MLT), from data obtained from 1998 to 2001 in Botucatu / SP / Brazil. The estimates were made using a series of combinations of astronomical and geographic variables by means of three artificial neural network (ANN) techniques such as MultLayer Perceptron (MLP), Radial Basis Functions Networks (RBF) and Generalized Regression Neural Network (GRNN) Adaptive Neuro Fuzzy Inference System (ANFIS). Statistical models (SM) of linear and polynomial regression were elaborated as reference. In Chapter 1 estimates of (Ktβh) were performed by combinations of variables measured and calculated from global horizontal surface irradiation (HgH) and estimates of (Kdβh) combinations of variables measured and calculated from (HgH) and global radiation on the sloped surface (Hgβ). In Chapter 2 estimates of (Kdβh) were performed by combinations of variables measured and calculated from the diffuse (HdH) and global (HgH) irradiances obtained on the horizontal surface. The statistical indicators r (correlation), RMSE (%) (precision) and MBE (%) (accuracy) were used to evaluate the results of the estimates. In Chapter 1 the best results in the estimates of (Ktβh) from the combinations performed with (HgH) were: MLP - RMSE = 3.73%; RBF - RMSE = 3.99%; GRNN - RMSE = 5.27%; ANFIS-RMSE = 3.78% and SM - RMSE = 6.65%. In this case the precision indicators show a reduction of approximately 44% with the use of the technique (MLP) in comparison to the statistical model (SM). In the estimates of (Kdβh) from the combinations of (HgH) the best results were: MLP - RMSE = 21.69%; RBF - RMSE = 25.43%; GRNN - RMSE = 29.39%; ANFIS - RMSE = 23.08% and SM - RMSE = 35.35%. Likewise, the precision indicators show a reduction of approximately 39% with the use of the technique (MLP) in comparison to the statistical model (SM). And in the estimates of (Kdβh) from the combinations performed with (Hgβ) the best results were: MLP - RMSE = 20.32%; RBF - RMSE = 21.95%; GRNN - RMSE = 29.11%; ANFIS - RMSE = 21.75% and SM - RMSE = 36.48%. The precision indicators show a reduction of approximately 44% with the use of the technique (MLP) in comparison to the statistical model (SM). In Chapter 2 the best estimates of (Kdβh) from the combinations performed with (HdH) were: MLP - RMSE = 4.03%; RBF - RMSE = 5.84%; GRNN - RMSE = 10.85%; ANFIS - RMSE = 4.15% and SM - RMSE = 12.42%. The precision indicators show a reduction of approximately 67% with the use of the technique (MLP) in comparison to the statistical model (SM). In the estimates of (Kdβh) from (HgH) the best results were: MLP - RMSE = 21.69%; RBF - RMSE = 25.43%; GRNN - RMSE = 29.39%; ANFIS - RMSE = 23.08% and SM - RMSE = 35.35%. The precision indicators show a reduction of approximately 39% with the use of the technique (MLP) in comparison to the statistical model (SM). The results show that the artificial neural network MLP technique presented the best indexes in all estimates of (Ktβh) and (Kdβh) with significant reductions when compared to the results obtained with the estimates obtained with the statistical models. By the analysis of the results it is possible to observe that the use of the machine learning techniques (MLT) in the combinations of proposed variables and the data obtained from Botucatu / SP, are presented as an alternative to the statistical models (SM) to estimate the variables of (Ktβh) and (Kdβh).
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28

Malladi, Vijaya Venkata Narasimha Sriram. "Development and Design of Self-Sensing SMAs using Thermoelectric Effect." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/33407.

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Active research of SMAs has shown that its Seebeck coefficient is sensitive to its martensitic phase transformation and has the potential to determine the SMAs state of transformation. The combination of Shape Memory Alloys, which have a positive Seebeck coefficient, and Constantan which has a negative Seebeck coefficient (-35 mV/K) results in a thermocouple capable of measuring temperature. The work presented in this thesis is based on the development and design of this sensor. This sensor is used to study the hysteretic behaviour of SMAs. Although Shape Memory Alloys (SMAs) exhibit a myriad of nonlinearities, SMAs show two major types of nonlinear hysteresis. During cyclic loading of the SMAs, it is observed that one type of hysteretic behavior depends on the rate of heating the SMAs, whilst the variation of maximum temperature of an SMA in each cycle results in the other hysteretic behavior. This later hysteretic behavior gives rise to major and minor nonlinear loops of SMAs. The present work analyzes the nonlinearities of hysteretic envelopes which gives the different maximum temperatures reached for each hysteretic cycle with respect to stress and strain of the SMA. This work then models this behavior using Adaptive Neuro Fuzzy Inference System (ANFIS) and compares it to experimental results. The nonlinear learning and adaptation of ANFIS architecture makes it suitable to model the temperature path hysteresis of SMAs.
Master of Science
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29

Lerkkasemsan, Nuttapol. "Modeling of Bioenergy Production." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/48591.

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In this dissertation we address three different sustainability concepts: [1] modeling of biodiesel production via heterogeneous catalysis, [2] life cycle analysis for pyrolysis of switchgrass for using in power plant, and [3] modeling of pyrolysis of biomass. Thus we deal with Specific Aim 1, 2 and 3. In Specific Aim 1, the models for esterification in biodiesel production via heterogeneous catalysis were developed. The models of the reaction over the catalysts were developed in two parts. First, a kinetic study was performed using a deterministic model to develop a suitable kinetic expression; the related parameters were subsequently estimated by numerical techniques. Second, a stochastic model was developed to further confirm the nature of the reaction at the molecular level. The deterministic and stochastic models were in good agreement. In Specific Aim 2, life cycle analysis and life cycle cost for pyrolysis of switchgrass for using in power plant model were developed. The greenhouse gas (GHG) emission for power generation was investigated through life cycle assessment. The process consists of cultivation, harvesting, transportation, storage, pyrolysis, transportation and power generation. Here pyrolysis oil is converted to electric power through co- combustion in conventional fossil fuel power plants. The conventional power plants which are considered in this work are diesel engine power plant, natural gas turbine power plant, coal-fired steam-cycle power plant and oil-fired steam-cycle power plant. Several scenarios are conducted to determine the effect of selected design variables on the production of pyrolysis oil and type of conventional power plants. In Specific Aim 3, pyrolysis of biomass models were developed. Since modeling of pyrolysis of biomass is complex and challenging because of short reaction times, temperatures as high as a thousand degrees Celsius, and biomass of varying or unknown chemical compositions. As such a deterministic model is not capable of representing the pyrolysis reaction system. We propose a new kinetic reaction model, which would account for significant uncertainty. Specifically we have employed fuzzy modeling using the adaptive neuro-fuzzy inference system (ANFIS) in order to describe the pyrolysis of biomass. The resulting model is in better agreement with experimental data than known deterministic models.
Ph. D.
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30

Madkour, A. A. M., M. Alamgir Hossain, Keshav P. Dahal, and H. Yu. "Real-time system identification using intelligent algorithms." IEEE, 2004. http://hdl.handle.net/10454/2471.

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This research presents an investigation into the development of real time system identification using intelligent algorithms. A simulation platform of a flexible beam vibration using finite difference (FD) method is used to demonstrate the real time capabilities of the identification algorithms. A number of approaches and algorithms for on line system identifications are explored and evaluated to demonstrate the merits of the algorithms for real time implementation. These approaches include identification using (a) traditional recursive least square (RLS) filter, (b) Genetic Algorithms (GAs) and (c) adaptive Neuro_Fuzzy (ANFIS) model. The above algorithms are used to estimate a linear discrete second order model for the flexible beam vibration. The model is implemented, tested and validated to evaluate and demonstrate the merits of the algorithms for real time system identification. Finally, a comparative performance of error convergence and real time computational complexity of the algorithms is presented and discussed through a set of experiments.
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31

Daher, Alaa. "Diagnostic et pronostic des défauts pour la maintenance préventive et prédictive. Application à une colonne de distillation." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMR090/document.

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Le procédé de distillation est largement utilisé dans de nombreuses applications telles que la production pétrochimique, le traitement du gaz naturel, les raffineries de pétrole, etc. Généralement, la maintenance des réacteurs chimiques est très coûteuse et perturbe la production pendant de longues périodes. Tous ces facteurs démontrent réellement la nécessité de stratégies efficaces de diagnostic et de pronostic des défauts pour pouvoir réduire et éviter le plus grand nombre de ces problèmes catastrophiques. La première partie de nos travaux vise à proposer une méthode de diagnostic fiable pouvant être utilisée dans le régime permanent d’une procédure non linéaire. De plus, nous proposons une procédure modifiée de la méthode MFCM permettant de calculer la variation en pourcentage entre deux classes. L’utilisation de MFCM a pour objectif de réduire le temps de calcul et d’accroître les performances du classifieur. Les résultats de la méthode proposée confirment la capacité de classifier entre les différentes classes de défaillances considérées. Le calcul de la durée de vie du système est extrêmement important pour éviter les pannes catastrophiques. Notre deuxième objectif est de proposer une méthode fiable de pronostic permettant d’estimer le chemin de dégradation d’une colonne de distillation et de calculer le pourcentage de durée de vie de ce système. Le travail présente une approche basée sur le système d’inférence neuro-fuzzy adaptatif (ANFIS) combiné avec (FCM) pour prédire la trajectoire future et calculer le pourcentage de durée de vie du système. Les résultats obtenus démontrent la validité de la technique proposée pour atteindre les objectifs requis avec une précision de haut niveau. Pour améliorer les performances d’ANFIS, nous proposons la distribution de Parzen comme nouvelle fonction d’appartenance de l’algorithme ANFIS. Les résultats ont démontré l’importance de la technique proposée car elle s’est avérée efficace pour réduire le temps de calcul. En outre, la distribution de Parzen présentait la plus petite erreur quadratique moyenne (RMSE). La dernière partie de cette thèse se concentrait sur la proposition d’un nouvel algorithme pouvant être appliqué pour obtenir un système de surveillance en temps réel s’appuyant sur la prédiction de défauts ; cela signifie que cette méthode permet de prédire l’état futur du système, puis de diagnostiquer quelle est la source d’erreur probable. Elle permet d’évaluer la dégradation d’une colonne de distillation et de diagnostiquer par la suite les défauts ou accidents pouvant survenir à la suite de la dégradation estimée. Cette nouvelle approche combine les avantages d’ANFIS à ceux de RNA permettant d’atteindre un haut niveau de précision
The distillation process is largely used in many applications such a petrochemical production, natural gas processing, and petroleum refineries, etc. Usually, maintenance of the chemical reactors is very costly and it disrupts production for long periods of time. All these factors really demonstrate the fundamental need for effective fault diagnosis and prognostic strategies that they are able to reduce and avoid the greatest number of thes problems and disasters. The first part of our work aims to propose a reliable diagnostic method that can be used in the steady-state regime of a nonlinear procedure. Moreover, we propose a modified procedure of the fuzzy c-means clustering method (MFCM) where MFCM calculates the percentage variation between the two clustered classes. The purpose of using MFCM is to reduce the computing time and increase the performance of the classifier. The results of the proposed method confirm the ability to classify between normal mode and eight abnormal modes of faults. Our second goal aims to propose a prognosis reliable method used to estimate the degradation path of a distillation column and calculate the lifetime percentage of this system. The work presents an approach based on adaptive neuro-fuzzy inference system (ANFIS) combined with (FCM) to predict the future path and calculate the lifetime percentage of the system. The results obtained demonstrate the validity of the proposed technique to achieve the needed objectives with a high-level accuracy. To improve ANFIS performance we propose Parzen windows distribution as a new membership function for ANFIS algorithm. Results demonstrated the importance of the proposed technique since it proved to be highly successful in terms of reducing the time consumed. Additionally, Parzen windows had the smallest Root Mean Square Error (RMSE). The last part of this thesis was focusing on the proposing of new algorithm which can be applied to obtain real-time monitoring system which relies on the fault production module to reach the diagnosis module in contrast to the previous strategies ; this means this method predict the future state of the system then diagnosis what is the probable fault source. This proposed method has proven to be a reliable process that can evaluate the degradation of a distillation column and subsequently diagnose the possible faults or accidents that can emerge as a result of the estimated degradation. This new approach combines the benefits of ANFIS with the benefits of feedforward ANN. The results were demonstrated that the technique achieved with a high level of accuracy, the objective of prediction and diagnosis especially when applied to the data obtained from automated distillation process in the chemical industry
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32

Sanches, Heleno da Luz Monteiro. "Optimização do despacho e reserva girante em sistemas eléctricos híbridos. Estudo de caso: sistema eléctrico da Ilha de Santiago em Cabo Verde." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8737.

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Dissertação para obtenção do grau de Mestre em Energias Renováveis – Conversão Eléctrica e Utilização Sustentáveis
Com os avanços conseguidos no campo de tecnologias de conversão de energias renováveis nos últimos 20 anos, e as escaladas no preço do petróleo dos últimos anos, tornou-se mais atractivo investir em tecnologias de conversão de energias renováveis, principalmente em sistemas eléctricos isolados de elevada disponibilidade de recursos renováveis, como é o caso do sistema eléctrico da ilha de Santiago em Cabo Verde, onde aumentou-se consideravelmente a penetração renovável nos últimos três anos. Contudo, sobretudo devido à variabilidade dos recursos e produção renovável, o aumento destas fontes nos sistemas eléctricos isolados acrescenta também desafios à tomada de decisão de optimização do despacho e reserva girante. Assim, é apresentado nesta dissertação um sistema inteligente que se baseia na lógica difusa (fuzzy logic) e sistema neuro-fuzzy (ANFIS) para optimizar automaticamente o despacho e reserva girante no Sistema Eléctrico Híbrido da Ilha de Santiago (SEHIS). O sistema proposto baseia-se na previsão do consumo e produção renovável, nomeadamente a produção eólica e fotovoltaica, e despacha automaticamente os geradores a fuelóleo com base nos seus custos de produção, por forma a permitir a máxima penetração renovável, reduzindo assim o consumo do fuelóleo e, consequentemente, o custo de produção. Além disso, o sistema proposto salvaguarda as restrições técnicas do sistema eléctrico, nomeadamente a reserva girante mínima necessária para fazer face à contingência ou erro de previsão, e ainda as restrições técnicas dos geradores, designadamente o limite mínimo de carga recomendado pelos fabricantes (50%), permitindo desta forma evitar a degradação da eficiência e aumento de avarias dos geradores.
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33

Aggab, Toufik. "Pronostic des systèmes complexes par l’utilisation conjointe de modèle de Markov caché et d’observateur." Thesis, Orléans, 2016. http://www.theses.fr/2016ORLE2051/document.

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Cette thèse porte sur le diagnostic et le pronostic pour l’aide à la maintenance de systèmes complexes. Elle présente deux approches de diagnostic/pronostic qui permettent de générer les indicateurs utiles pour l’optimisation de la stratégie de maintenance. Plus précisément, ces approches permettent d’évaluer l’état de santé et de prédire la durée de vie résiduelle du système. Les approches présentées visent en particulier à pallier le problème d’absence d’indicateurs de dégradation. Les développements sont fondés sur l’utilisation d’observateurs, de formalisme de Modèle de Markov Caché, des méthodes d’inférences statistiques et des méthodes de prédiction de séries temporelles à base d’apprentissage afin de caractériser et prédire les modes de fonctionnement du système. Les deux approches sont illustrées sur des exemples de dégradation d’un système de régulation de niveau d’eau, d’une machine asynchrone et d’une batterie Li-Ion
The research presented in this thesis deals of diagnosis and prognosis of complex systems. It presents two approaches that generate useful indicators for optimizing maintenance strategies. Specifically, these approaches are used to assess the level of degradation and estimate the Remaining Useful Life of the system. The aim of these approaches is to overcome for the lack of degradation indicators. The developments are based on observers, Hidden Markov Model formalism, statistical inference methods and learning-based methods in order to characterize and predict the system operating modes. To illustrate the proposed failure diagnosis/prognosis approaches, a simulated tank level control system, an induction motor and a Li-Ion battery were used
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34

Keneni, Blen M. Keneni. "Evolving Rule Based Explainable Artificial Intelligence for Decision Support System of Unmanned Aerial Vehicles." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525094091882295.

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35

Khanfar, Ahmad A. "Forecasting failure of information technology projects using an adaptive neuro-fuzzy inference system." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2019. https://ro.ecu.edu.au/theses/2262.

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The role of information technology (IT) applications has become critical for organisations in various sectors such as education, health, finance, logistics, manufacturing and project management. IT applications provide many advantages at strategic, management and operational levels, and the investment in IT applications is therefore growing; however, the failure rate of IT projects is still high, despite the development of theories, methodologies and frameworks for IT project management in recent decades. The consequences of failure of an IT project can be devastating, and can threaten the existence of an organisation. There are many different factors that impact on the performance of a project; these factors are varied and interrelated, and can impact project performance throughout the different phases of the project life cycle. The aims of this research are to (i) identify the critical failure factors (CFFs) of IT projects; (ii) categorise these CFFs; (iii) identify the relationships between CFFs; and (iv) develop a model using an adaptive neuro-fuzzy inference system (ANFIS) to forecast the failure of IT projects in the early stages. The primary data collection tool is a questionnaire, and the analysis is carried out with the ANFIS technique. ANFIS is a hybrid model that combines an artificial neural network (ANN) with learning algorithms and techniques, and uses fuzzy logic to extract fuzzy rules based on prior knowledge of past data. In this research, we develop 266 rules and then test the performance of the developed model using training data and checking data. In this way, the role structure of the ANFIS model is obtained, which can be used to forecast the failure of IT projects. The findings suggest that there are many failure factors that can impact negatively on the performance of IT projects. These factors can be categorised into organisational, project management, planning, project manager, project team, user/customer, technological and technical, and legal factors. The results show that CFFs related to the project team, planning and organisation have the highest impact on the failure of IT projects. The ANFIS model constructed here can help IT project managers to effectively address the risk associated with projects in the early phases and to forecast the failure percentage of IT projects. This research can enable managers and decision makers to predict failure early in the project, allowing them to take suitable decisions, and can provide policy makers with an innovative approach to enhance decision-making processes
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36

Cocheteux, Pierre. "Contribution à la maintenance proactive par la formalisation du processus de pronostic des performances de systèmes industriels." Phd thesis, Université Henri Poincaré - Nancy I, 2010. http://tel.archives-ouvertes.fr/tel-00545249.

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Les contraintes des marchés et les attentes de la société vis-à-vis des systèmes industriels en termes économique, sécuritaire, environnementaux... requièrent de considérer les performances de ces derniers de façon globale sur l'ensemble de leur cycle de vie. Cela nécessite de mettre en synergie, par exemple avec des ingénieries couplées dès la conception, le système principal et ses systèmes contributeurs, et notamment celui de soutien avec son processus pivot de maintenance. Cette focalisation intégrative sur la maintenance a conduit à évoluer d'anciennes pratiques de maintenance vers de nouvelles plus proactives faisant émerger des stratégies prévisionnelles dont le processus clé est le pronostic. Cependant ce processus fait l'objet d'un réel manque de formalisation et les travaux existants restent principalement centrés sur les composants, sans prendre en compte les performances des systèmes. Ainsi notre contribution porte sur la proposition d'architectures génériques de pronostic système permettant d'obtenir les évolutions futures des dégradations/défaillances des composants et des performances de niveaux système/sous-systèmes/composants : soit directement par un pronostic adapté, soit par modélisation de la causalité dysfonctionnelle sous forme de relations logiques supportées par un réseau de neurones flou ANFIS. Une méthodologie est associée pour définir les indicateurs de dégradation et de performance, aboutissant à la réalisation des architectures. Enfin la faisabilité de cette approche est démontrée sur un système de déroulage/pressage de la plateforme TELMA.
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37

Chotikorn, Nattapong. "Implementations of Fuzzy Adaptive Dynamic Programming Controls on DC to DC Converters." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1505139/.

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DC to DC converters stabilize the voltage obtained from voltage sources such as solar power system, wind energy sources, wave energy sources, rectified voltage from alternators, and so forth. Hence, the need for improving its control algorithm is inevitable. Many algorithms are applied to DC to DC converters. This thesis designs fuzzy adaptive dynamic programming (Fuzzy ADP) algorithm. Also, this thesis implements both adaptive dynamic programming (ADP) and Fuzzy ADP on DC to DC converters to observe the performance of the output voltage trajectories.
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38

Lopes, Jos? Soares Batista. "Estudo e implementa??o da t?cnica de intelig?ncia artificial para o controle de velocidade do motor-mancal com bobinado dividido utilizando o DSP TMS3208F28335." PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA EL?TRICA E DE COMPUTA??O, 2016. https://repositorio.ufrn.br/jspui/handle/123456789/21802.

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Este trabalho descreve o estudo e a implementa??o digital embarcado em um DSP TMS 3208F28335 para o controle vetorial de velocidade do motor-mancal com bobinado dividido de 4 p?los com 250W de pot?ncia. As t?cnicas inteligentes: ANFIS e as Redes Neurais foram investigadas e implementadas computacionalmente para a avalia??o do desempenho do motor-mancal nas seguintes condi??es: operando como estimador de par?metros incertos, e como controlador de velocidade, respectivamente. Para isso, utilizou-se o programa MATLAB? e seu toolbox para as simula??es e os ajustes dos par?metros envolvendo a estrutura ANFIS, e tamb?m para as simula??es com a Rede Neural. Os resultados simulados mostraram um bom desempenho para as duas t?cnicas aplicadas, de forma diferente: como estimador, e como controlador de velocidade utilizando ambas um modelo do motor de indu??o operando como um motor-mancal. A parte experimental para o controle vetorial de velocidade utiliza tr?s malhas de controles: corrente, posi??o radial e velocidade, onde foram investigados a configura??o dos perif?ricos, as interfaces ou drivers para o acionamento do motor-mancal. Detalhes de configura??o dos perif?ricos do DSP TMS 3208F335 s?o descritas neste trabalho, assim como, as interfaces respons?veis pela aquisi??o da corrente, posi??o radial e velocidade do rotor. Por ?ltimo, s?o mostrados os resultados experimentas do motor-mancal comparando o funcionamento do controle vetorial cl?ssico com o controle neural.
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39

Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.

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Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.
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40

Cardozo, Gálvez Erick Octavio. "Diseño de un GPC con Restricciones Basado en un Modelo ANFIS para el Control del Proceso de Neutralización del pH en los Efluentes Residuales de una Planta Concentradora de Minerales Polimetálicos." Master's thesis, Pontificia Universidad Católica del Perú, 2018. http://tesis.pucp.edu.pe/repositorio/handle/123456789/12058.

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Actualmente, la necesidad de proteger los recursos hídricos debido a la explotación indiscriminada y altos índices de contaminación ha obligado a la comunidad científica a buscar soluciones que posibiliten mitigar estas prácticas. El agua potable constituye un recurso vital que se encuentra en crisis. Para su protección se han creado organizaciones que fiscalizan su uso en las industrias, agricultura y minería. Se busca alternativas de solución de este problema para evitar pérdidas de vidas humanas y económicas, y/o conflictos sociales. En esta tesis se investiga la problemática relacionada con el control efectivo del proceso de neutralización del pH en los efluentes residuales de una planta concentradora de minerales polimetálicos, lo cual posibilita reutilizar el agua, evitando la contaminación de los lagos y/o lagunas cercanas con las aguas resultantes del proceso minero. Se desarrolla el diseño de un controlador predictivo generalizado (GPC) basado en un modelo con arquitectura adaptativa de inferencia neuro-difusa (ANFIS - Adaptive Neuro Fuzzy Inference System), es decir un controlador GPC-ANFIS. Considerando la alta no linealidad que presenta el comportamiento dinámico de la planta objeto de estudio, la arquitectura ANFIS posibilita obtener una cierta cantidad de modelos lineales en todo el rango de operación de dicha planta, es decir se divide el comportamiento dinámico de la planta en determinados modelos lineales, y cada uno de estos modelos forma parte de la estructura del controlador GPC-ANFIS. El controlador que se diseña presenta restricciones en el incremento de la señal de control, para lo cual se utiliza un algoritmo de programación cuadrática sujeta a restricciones, lo que posibilita el funcionamiento de la planta dentro de los límites de variación del pH requeridos (entre 7 – 8). Finalmente, se realiza un análisis de robustez del controlador diseñado, así como una propuesta de su implementación práctica.
Tesis
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41

Vasile, Dragomir Otilia Elena. "Contribution au pronostic de défaillances par réseau neuro-flou : maîtrise de l'erreur de prédiction." Phd thesis, Université de Franche-Comté, 2008. http://tel.archives-ouvertes.fr/tel-00362509.

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L'activité de "pronostic de défaillances" est aujourd'hui considérée comme un processus clef dans les stratégies de maintenance industrielle. Cependant, dans la pratique, les outils de pronostic sont encore rares. Les approches aujourd'hui stabilisées reposent sur un historique des incidents assez conséquent pour être représentatif des événements potentiellement prévisibles. L'objet de cette thèse est de proposer un "outil" permettant de prédire la dégradation d'un équipement sans connaissance a priori sur son comportement, et de générer les indicateurs de pronostic permettant d'optimiser les stratégies de maintenance. Dans cet objectif, notre contribution se décline en trois aspects complémentaires.
- Un premier volet de travail traite de la formalisation du processus de pronostic. Le concept de pronostic est défini et positionné par rapport aux stratégies de maintenance. Différents mesures typées pronostic sont proposées et les outils utilisables dans ce contexte sont étudiés (nature, applicabilité, guide de choix).
- Le coeur du travail porte ensuite sur la spécification d'un système neuro-flou permettant de reproduire l'évolution des propriétés d'un équipement, et de prédire un état de dégradation au cours du temps. Plus précisément les développements visent la proposition d'une architecture neuro-floue permettant de satisfaire un objectif de contrôle de l'erreur de prédiction, quel que soit l'horizon de prédiction.
- Nous développons finalement une approche floue/possibiliste d'adaptation des processus classiques d'évaluation prévisionnelle des grandeurs de sûreté de fonctionnement au cas prédictif (fiabilité, MTTF). Ces indicateurs doivent permettre in fine d'optimiser les stratégies de maintenance en tenant compte de l'incertitude inhérente à l'étape de prédiction des dégradations.
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42

Guruprasad, K. R. "Model Reference Learning Control Using ANFIS." Thesis, 1996. https://etd.iisc.ac.in/handle/2005/1714.

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43

Guruprasad, K. R. "Model Reference Learning Control Using ANFIS." Thesis, 1996. http://etd.iisc.ernet.in/handle/2005/1714.

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44

Chen, Wei-min, and 陳偉民. "A Neural Network Structure Based on the ANFIS." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/64446483410059443947.

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碩士
實踐大學
資訊科技與管理學系碩士班
99
Fuzzy theory is a mathematical model to represent the uncertainty of human cognitive processes. The neural network is a computing system. It uses a large number of parallel distributed and connected artificial neurons to imitate the ability of biological neural networks. Fuzzy systems and neural networks have been successfully applied in various fields, and achieved very good results. Adaptive Neural Fuzzy Inference System (ANFIS) is one kind of neural fuzzy systems. It solves the problems of establishing IF-THEN fuzzy rules in traditional fuzzy systems. However, due to its learning structure, the ANFIS suffers from the "curse of dimensionality". That is, the number of IF-THEN rules will grow exponentially with the increase of the number of input variables. To solve the problem of “curse of dimensionality”, this research proposed the use of low-dimensional ANFIS (LDANFIS). LDANFIS is composed of several modules. The output of LDANFIS is the sum of outputs from these modules. Each module consists of several small ANFISs. The output of each module is the product of outputs from these small ANFISs inside the module. Since each small ANFIS uses only a portion of input variables as its inputs, the number of IF-THEN rules will be greatly reduced. The learning rules for LDANFIS were deducted by the gradient descent method. To evaluate the feasibility of the LDANFIS, many practical computer simulations were made. The LDANFIS was applied to solve high-dimensional function approximation problems, Mackey-Glass time series problem, and classification problems. Simulations showed promising results with the LDANFIS applied in high-dimensional problems.
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45

Chin-Tsao, Huang, and 黃津操. "Recognition the P300 of Brain Wave Via ANFIS." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/85600876971882411950.

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碩士
國立臺灣師範大學
機電科技研究所
93
The brain computer interface is a technology that through human being’s brain to control tools ( machine、wheel chair etc.), by using a recognition system for brain, the users can communicate with others through their thoughts. The technology can be used to help the patients who can’t move by themselves but the functions of their brains are fine. They can do what they want to do depending on this technology. The brain computer interface is appropriate for this application. Our research uses visual evoked feedback system to evoke brain wave and we extract the P300 signals as the input to the classifier Adaptive Neuro-Fuzzy Interface System, the results after classifying can be taken as the control sources of the brain computer interface. Our purpose is to improve the classifying accuracy for the brain computer interface. The experiment results show that the average accuracy of single brain wave is 79% and the best accuracy is 83%, the average of two brain waves is 87% and the best accuracy is 95%. Nevertheless, since the subjects of our research are healthy and the data we used are those without disturbance, the data of the study should be cautiously made. Our research is beneficial for people who are interested in brain computer interface.
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46

Wei, Liang-ying, and 魏良穎. "ANFIS-Based Fusion Model for Stock Index Forecasting." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/15708019495865804932.

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博士
雲林科技大學
資訊管理系博士班
97
Forecasting stock index accurately is not an easy task because there are many market variables whose structural relationships are not perfectly linear and market environment changes dramatically in minute. Traditional time-series models had been presented to deal with forecasting problems however the linear models such as autoregressive (AR), fail to deal with nonlinear relationships. Conventional time-series models are unable to treat the issues with linguistic historical data, therefore, many fuzzy forecasting models are proposed. Most previous studies of time-series models utilize only one variable (stock price) in stock forecasting, and few consider multiple variables for stock market such as the volatility of highly related foreign stock markets. To reconcile drawbacks mentioned above, this dissertation proposed an ANFIS (adaptive network based fuzzy inference system) based fusion model which fusions linear relationships (autoregressive model, adaptive expectation model (AEM)) and non-linear relationships (volatility of stock index, adaptive network) to forecast stock index. For improving the forecasting performance of time-series model, the volatility of highly related foreign stock markets is incorporated into proposed model. Then, this dissertation proposes a set of refined models which include six models: (1) AR + ANFIS model, (2) Multi-stock volatility + ANFIS model, (3) AR + volatility + ANFIS model, (4) AR + ANFIS + AEM model, (5) Multi-stock volatility + ANFIS +AEM model and (6) AR + volatility + ANFIS + AEM model. To verify the proposed models carefully, a seven-year period of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and a five-year period of Hang Seng Index (HSI) are employed as experimental datasets. The results indicate that the proposed models outperform the listing models. In practical applications, the proposed models can extract few linguistic rules for investors to make decisions of investment in stock markets.
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Hiremath, Shrishailayya. "ANFIS Based Data Rate Prediction For Cognitive Radio." Thesis, 2010. http://ethesis.nitrkl.ac.in/1991/1/SHRI_FINAL_THEIS__(208EC110).pdf.

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Intelligence is needed to keep up with the rapid evolution of wireless communications, especially in terms of managing and allocating the scarce, radio spectrum in the highly varying and disparate modern environments. Cognitive radio systems promise to handle this situation by utilizing intelligent software packages that enrich their transceiver with radio-awareness, adaptability and capability to learn. A cognitive radio system participates in a continuous process, the ‘‘cognition cycle”, during which it adjusts its operating parameters, observes the results and, eventually takes actions, that is to say, decides to operate in a specific radio configuration (i.e., radio access technology, carrier frequency, modulation type, etc.) expecting to move the radio toward some optimized operational state. In such a process, learning mechanisms utilize information from measurements sensed from the environment, gathered experience and stored knowledge and guide in decision making. This thesis introduces and evaluates learning schemes that are based on adaptive neuro-fuzzy inference system (ANFIS) for predicting the capabilities (e.g. data rate) that can be achieved by a specific radio configuration in cognitive radio. First a ANFIS based scheme is proposed. The work reported here is compare previous neural network based learning schemes. Cognitive radio is a intelligent emergent technology, where learning schemes are needed to assist in its functioning. ANFIS based scheme is one of the good learning Artificial intelligence method, that combines best features of neural network and fuzzy logic. Here ANFIS and neural networks methods are able to assist a cognitive radio system to help in selecting the best one radio configuration to operate in. Performance metric like RMSE, prediction accuracy of ANFIS learning has been used as performance index.
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48

Hua, Ming-yao, and 花明耀. "ANFIS-Based Rate Compatible LDPC code for LMDS systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/4wncyz.

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碩士
國立中央大學
通訊工程研究所碩士在職專班
96
Local Multipoint Distribution Service（LMDS）is the point to multi-point fixed wireless broadband access system and it is two-way digital cellular system operating in 20-50 GHz to provide high-speed service，such as broadband access 、data transmission、 two-way audio、 video、multimediaetc. Inter cell interference (ICI) and rain attenuation influence are the two major elements of Quality of Service (QoS) in LMDS system. In the thesis to analysis of performance to such three pieces of influence LMDS systems efficiency parameter as Beamwith ,rain attenuation, XPD, etc. and discover the rain attenuation is the major factors of definitive system performance in LMDS systems. In order to reduce the impacts on rain attenuation ， this thesis adopt mighty channel coding technologies Low Density Parity Check codes，Its error correction ability may approach shannon limit and propose Rate Compatible LDPC codes provide various coding rates and users can select suitable coding rates according to channel conditions, with respect to the rain attenuation to improve the performance of LMDS systems. The better the quality is, higher code rates are used to reduce the redundancy bit. As to the quality of channel conditions, we adopt ANFIS modeled the data with its own automatically created fuzzy rules to judge . Simulation results show that the proposed ANFIS-Based rate compatible LDPC code for LMDS systems compared with uncompatible LDPC code scheme leading improvement of Throughput. But not to loss the BER .
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49

Lee, Wei-Chi, and 李韋奇. "Forecasting Volatility of Stock Market Using ANFIS-GARCH Model." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/16922803585044396863.

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碩士
嶺東科技大學
資訊科技應用研究所
97
In the process of data analysis for time sequence, it is often finding the case that the conditional volatility unmatched the assumption of constant volatility according to traditional econometrics models. The volatility always be affected with past price fluctuation and time then cause so called “volatility clustering”. The volatility clustering is the price fluctuation in the latest financial market but also affected with the previous one. Under this circumstance, usually using GARCH(Generalized Autoregressive Conditional Heteroscedasticity) model to calculate the influence of volatility could get the best result. But GARCH model is representing the residual power of past impact and unable to show the immediate dramatic market change or irregular date change, such as the up and down in the stock market. In this paper, we’ll use GARCH through ANFIS (Adaptive Neuro-Fuzzy Inference System) to testify. And multiply GARCH to explain the irregular data in the stock market and even make forecasting for the volatility. The data consist of daily closing values for three stock indices from February 1, 2001, through May 31, 2009. We focus on the Taiwan, NASDAQ, and Japan to illustrate the performance of the proposed method. The first half (February 1, 2001, through March 31, 2007) of the sample makes up the estimation period, while the second half (April 1, 2007, through May 31, 2009) is the forecasting period. Also we classify the information into three parts, short term (one quarter), semi mid-term (one year), and mid-term (two year) just to prove our prediction.
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50

Yeh, Yow-Cheng, and 葉宥呈. "Application of ANFIS to Intrusion Detection System of WLAN." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/89474894101262493230.

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Abstract:
碩士
元智大學
通訊工程學系
97
The intruders may attack the medium access control (MAC) layer of a WiFi network using forged de-authentication frames that cause clients to disconnect from an access point (AP). The non-parametric sequential change point detection (NPSCPD) methodology detects the de-authentication denial-of-service (DoS) attacks and maintains the average false alert rate (FAR) below a prescribed low level. But its average detection delay (ADD) is too long to efficiently provide compensation before the network is disabling. When the wireless local-area networks (WLANs) are attacked, the sequence number value in the packets varies abnormally. In this thesis, the packet collection and frame content analyzing system for the MAC layer of 802.11b/g WLAN is constructed on x86 embedded system. Based on adaptive neuro-fuzzy inference system (ANFIS) rule, the change value of the packet sequence number, de-authentication frames and the NPSCPD algorithm are used to reduce the ADD of the network intrusion detection system. Finally, the simulated observation data are used to test the FAR and ADD performance of the proposed intrusion detection system.
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