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Academic literature on the topic 'ANFIS'

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Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "ANFIS"

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Yeom, Chan-Uk, and Keun-Chang Kwak. "Performance Comparison of ANFIS Models by Input Space Partitioning Methods." Symmetry 10, no. 12 (December 3, 2018): 700. http://dx.doi.org/10.3390/sym10120700.

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In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a clustering method that considers the characteristics of the output space as well as the input space. Here, the symmetric Gaussian membership functions are obtained by the clusters produced from each context in the design of the ANFIS4. In order to evaluate the performance of the ANFIS models according to the input space segmentation method, a prediction experiment was conducted using the combined cycle power plant (CCPP) data and the auto-MPG (miles per gallon) data. As a result of the prediction experiment, we confirmed that the ANFIS4 model using the proposed input space segmentation method shows better prediction performance than the ANFIS model (ANFIS1, ANFIS2, ANFIS3) using the existing input space segmentation method.
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Mousavi, S. F., and M. J. Amiri. "Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system." Soil and Water Research 7, No. 2 (May 18, 2012): 73–83. http://dx.doi.org/10.17221/46/2010-swr.

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High nitrate concentration in groundwater is a major problem in agricultural areas in Iran. Nitrate pollution in groundwater of the particular regions in Isfahan province of Iran has been investigated. The objective of this study was to evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) for estimating the nitrate concentration. In this research, 175 observation wells were selected and nitrate, potassium, magnesium, sodium, chloride, bicarbonate, sulphate, calcium and hardness were determined in groundwater samples for five consecutive months. Electrical conductivity (EC) and pH were also measured and the sodium absorption ratio (SAR) was calculated. The five-month average of bicarbonate, hardness, EC, calcium and magnesium are taken as the input data and the nitrate concentration as the output data. Based on the obtained structures, four ANFIS models were tested against the measured nitrate concentrations to assess the accuracy of each model. The results showed that ANFIS1 was the most accurate (RMSE = 1.17 and R<sup>2</sup> = 0.93) and ANFIS4 was the worst (RMSE = 2.94 and R<sup>2</sup> = 0.68) for estimating the nitrate concentration. In ranking the models, ANFIS2 and ANFIS3 ranked the second and third, respectively. The results showed that all ANFIS models underestimated the nitrate concentration. In general, the ANFIS1 model is recommendable for prediction of nitrate level in groundwater of the studied region.
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Yeom, Chan-Uk, and Keun-Chang Kwak. "Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach." Applied Sciences 10, no. 23 (November 27, 2020): 8495. http://dx.doi.org/10.3390/app10238495.

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We propose an adaptive neuro-fuzzy inference system (ANFIS) with an incremental tree structure based on a context-based fuzzy C-means (CFCM) clustering process. ANFIS is a combination of a neural network with the ability to learn, adapt and compute, and a fuzzy machine with the ability to think and to reason. It has the advantages of both models. General ANFIS rule generation methods include a method employing a grid division using a membership function and a clustering method. In this study, a rule is created using CFCM clustering that considers the pattern of the output space. In addition, multiple ANFISs were designed in an incremental tree structure without using a single ANFIS. To evaluate the performance of ANFIS in an incremental tree structure based on the CFCM clustering method, a computer performance prediction experiment was conducted using a building heating-and-cooling dataset. The prediction experiment verified that the proposed CFCM-clustering-based ANFIS shows better prediction efficiency than the current grid-based and clustering-based ANFISs in the form of an incremental tree.
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Tien Bui, Dieu, Khabat Khosravi, Shaojun Li, Himan Shahabi, Mahdi Panahi, Vijay Singh, Kamran Chapi, et al. "New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling." Water 10, no. 9 (September 7, 2018): 1210. http://dx.doi.org/10.3390/w10091210.

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This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating characteristic (ROC) curve (area under the ROC (AUROC)) were used. Results showed that ANFIS-IWO with lower RMSE (0.359) had a better performance, while ANFIS-BA with higher AUROC (94.4%) showed a better prediction capability, followed by ANFIS0-IWO (0.939) and ANFIS-CA (0.921). These models can be suggested for FSM in similar climatic and physiographic areas for developing measures to mitigate flood damages and to sustainably manage floodplains.
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A-Matarneh, Feras Mohammed, Bassam A. Y. Alqaralleh, Fahad Aldhaban, Esam A. AlQaralleh, Anil Kumar, Deepak Gupta, and Gyanendra Prasad Joshi. "Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks." Computational Intelligence and Neuroscience 2022 (May 13, 2022): 1–11. http://dx.doi.org/10.1155/2022/7940895.

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Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such as fuzzy logic and swarm intelligence (SI) algorithms. With this motivation, this article aims to focus on the design of swarm intelligence with an adaptive neuro-fuzzy inference system-based routing (SI-ANFISR) protocol for clustered WSN. The proposed SI-ANFISR technique aims to determine the cluster heads (CHs) and optimal routes for multihop communication in the network. To accomplish this, the SI-ANFISR technique primarily employs a weighted clustering algorithm to elect CHs and construct clusters. Besides, the SI-ANFISR technique involves the design of an ANFIS model for the selection process, which make use of three input parameters, namely, residual energy, node degree, and node history. In order to optimally adjust the membership function (MF) of the ANFIS model, the squirrel search algorithm (SSA) is utilized. None of the earlier works have used ANFIS with SSA for the routing process. The design of SSA to tune the MFs of the ANFIS model for optimal routing process in WSN shows the novelty of the study. The experimental validation of the SI-ANFISR technique takes place, and the results are inspected under different aspects. The simulation results highlighted the significant performance of the SI-ANFISR technique compared to the recent techniques with a maximum throughput of 43838 kbps and residual energy of 0.4800J, respectively.
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Rezk, Hegazy, A. G. Olabi, Mohammad Ali Abdelkareem, Abdul Hai Alami, and Enas Taha Sayed. "Optimal Parameter Determination of Membrane Bioreactor to Boost Biohydrogen Production-Based Integration of ANFIS Modeling and Honey Badger Algorithm." Sustainability 15, no. 2 (January 13, 2023): 1589. http://dx.doi.org/10.3390/su15021589.

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Hydrogen is a new promising energy source. Three operating parameters, including inlet gas flow rate, pH and impeller speed, mainly determine the biohydrogen production from membrane bioreactor. The work aims to boost biohydrogen production by determining the optimal values of the control parameters. The proposed methodology contains two parts: modeling and parameter estimation. A robust ANIFS model to simulate a membrane bioreactor has been constructed for the modeling stage. Compared with RMS, thanks to ANFIS, the RMSE decreased from 2.89 using ANOVA to 0.0183 using ANFIS. Capturing the proper correlation between the inputs and output of the membrane bioreactor process system encourages the constructed ANFIS model to predict the output performance exactly. Then, the optimal operating parameters were identified using the honey badger algorithm. During the optimization process, inlet gas flow rate, pH and impeller speed are used as decision variables, whereas the biohydrogen production is the objective function required to be maximum. The integration between ANFIS and HBA boosted the hydrogen production yield from 23.8 L to 25.52 L, increasing by 7.22%.
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Abdallah, El, Maamar Laidi, and Salah Hanini. "New method based on neuro-fuzzy system and PSO algorithm for estimating phase equilibria properties." Chemical Industry and Chemical Engineering Quarterly, no. 00 (2021): 24. http://dx.doi.org/10.2298/ciceq201104024a.

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The subject of this work is to propose a new method based on ANFIS system and PSO algorithm to conceive a model for estimating the solubility of solid drugs in sc-CO2. The high nonlinear process was modeled by neuro-fuzzy approach (NFS). The PSO algorithm was used for two purposes: replacing the standard back propagation in training the NFS and optimizing the process. The validation strategy have been carried out using a linear regression analysis of the predicted versus experimental outputs. The ANFIS approach is compared to the ANN in terms of accuracy. Statistical analysis of the predictability of the optimized model trained with PSO algorithm (ANFIS-PSO) shows very good agreement with reference data than ANN method. Furthermore, the comparison in terms of AARD deviation (%) between the predicted results, results predicted by density-based models and a set of equations of state demonstrates that the ANFIS-PSO model correlates far better the solubility of the solid drugs in scCO2.A control strategy was also developed for the first time in the field of phase equilibrium by using the neuro fuzzy inverse approach (ANFISi) to estimate pure component properties from the solubility data without passing through GCM methods.
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Rezakazemi, Mashallah, Amir Dashti, Morteza Asghari, and Saeed Shirazian. "H2-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS, GA-ANFIS." International Journal of Hydrogen Energy 42, no. 22 (June 2017): 15211–25. http://dx.doi.org/10.1016/j.ijhydene.2017.04.044.

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Bassetto, Edson Luis, João Francisco Escobedo, and Alexandre Dal Pai. "ESTIMATIVA DA FRAÇÃO DIFUSA DA IRRADIAÇÃO GLOBAL COM TÉCNICAS DE APRENDIZAGEM DE MÁQUINAS." Revista Brasileira de Energia Solar 9, no. 2 (February 13, 2023): 127–36. http://dx.doi.org/10.59627/rbens.2018v9i2.242.

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Foram desenvolvidos modelos de estimativa da fração difusa (Kd) em função da fração transmitida da irradiação global (Kt) na partição horária sendo: Modelo Estatístico (ME); Redes Neurais Artificias com Função de Base Radial (RBF); e Sistema Adaptativo de Inferência Neuro Fuzzy (ANFIS). O modelo estatístico utiliza como referência somente Kt e as técnicas uma combinação de seis (06) variáveis astronômicas, geográficas e meteorológicas. Os modelos utilizam uma base de sete anos (2000-2006) de medidas realizadas na Estação de Radiometria Solar de Botucatu/SP na partição horária, sendo parte para treinamento e outra para validação dividida em Ano típico (AT) e atípico (AAT). A equação do modelo estatístico gerada por regressão polinomial de 4ª ordem, apresenta coeficiente de determinação R2 = 0.80 e na comparação dos valores medidos e estimados na validação, um coeficiente de correlação para ano típico (AT) rAT=0.90 e para o ano atípico (AAT) de rAAT=0.89, erro quadrático médio rRMSEAT = 30.55% e rRMSEAAT = 27.97%. No desempenho das técnicas RBF e ANFIS, os modelos mostraram-se satisfatórios a partir da segunda combinação sendo para RBF2 um coeficiente rAT=0.91 e rAAT=0.90 e erro de rRMSEAT = 29.63% e rRMSEAAT = 26.93% e para ANFIS2 um rAT=0.93 e rAAT=0.93 com erro rRMSEAT = 25.13% e rRMSEAAT = 22.76%. Para sexta combinação, a rede RBF6 um coeficiente de rAT=0.92 e rAAT=0.92 e erro de rRMSEAT = 26.48% e rRMSEAAT = 24.69% e para ANFIS6 um coeficiente rAT=0.95 e rAAT=0.94 e erro de rRMSEAT = 22.63% e rRMSEAAT = 21.19%. Os indicadores mostram que as técnicas de aprendizagem de máquinas comparadas com modelo estatístico apresentaram um desempenho melhor com redução nos indicadores na ordem de 16% para rede RBF6 e 34% para rede ANFIS6 do erro quadrático médio para duas bases de validação (AT e AAT) em relação ao modelo ME.
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Zhang, X. Y., and B. Wei. "A OPTIMIZATION TUNED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR DAM DEFORMATION PREDICTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1207–13. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1207-2020.

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Abstract. The performance and stability of Adaptive Neuro-Fuzzy Inference System (ANFIS) depend on its network structure and preset parameter selection, and Particle Swarm Optimization-ANFIS (PSO-ANFIS) easily falls into the local optimum and is imprecise. A novel ANFIS algorithm tuned by Chaotic Particle Swarm Optimization (CPSO-ANFIS) is proposed to solve these problems. A chaotic ergodic algorithm is first used to improve the PSO and obtain a CPSO algorithm, and then the CPSO is used to optimize the parameters of ANFIS to avoid falling into the local optimum and improve the performance of ANFIS. Based on the deformation data from the Xiaolangdi Dam in China, three neural network algorithms, ANFIS, PSO-ANFIS, and CPSO-ANFIS, are used to establish the dam deformation prediction models after data preparation and selection of influencing factors for the dam deformation. The results are compared using evaluation indicators that show that CPSO-ANFIS is more accurate and stable than ANFIS and PSO-ANFIS both in predictive ability and in predicted results.
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Dissertations / Theses on the topic "ANFIS"

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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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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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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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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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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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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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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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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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Made available in DSpace on 2014-06-12T17:39:51Z (GMT). No. of bitstreams: 2 arquivo6971_1.pdf: 519832 bytes, checksum: 35de3846e1bc6e866dd2ee8b7a6bc74b (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2006
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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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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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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Books on the topic "ANFIS"

1

Suparta, Wayan, and Kemal Maulana Alhasa. Modeling of Tropospheric Delays Using ANFIS. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28437-8.

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Shu, Chang. Estimation régionale des débits de crues par la méthode ANFIS. Québec: INRS, Eau, terre et environnement, 2007.

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K, Kokula Krishna Hari, ed. Hybrid Energy System fed ANFIS based SEPIC Converter for DC/AC Loads. Chennai, India: Association of Scientists, Developers and Faculties, 2016.

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Neelanarayanan, ed. Multi-step Prediction of Pathological Tremor With Adaptive Neuro Fuzzy Inference System (ANFIS). VIT University Chennai, India: Association of Scientists, Developers and Faculties, 2014.

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Elżbieta, Delis-Modzelewska, ed. Anais: Życie erotyczna Anais Nin. Warszawa: Sic!, 2006.

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Seminário sobre Preservação de Bens Culturais (1st 1989 São Paulo, Brazil). Anais. São Paulo: Universidade de São Paulo, Sistema Integrado de Bibliotecas, 1989.

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Serra, Brazil) Simpósio de Biologia de Mato Grosso (1st 2004 Tangará da. Anais. Cáceres, MT: Universidade do Estado de Mato Grosso, Instituto de Ciências Naturais e Tecnológicas, 2004.

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Fórum, de Assessorias das Universidades Brasileiras para Assuntos Internacionais (6th 1994 Belém Brazil). Anais. Belém, Pará, Brasil: Universidade Federal do Pará, Assessoria de Relações Nacionais e Internacionais, 1996.

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Almeida, Silva Denise, Nascimento Franciele da Silva, Reinheimer, Magali Teresa de Pellegrin, and Universidade Regional Integrada. Campus de Frederico Westphalen. Programa de Pós-Graduação em Letras, eds. Anais. Frederico Westphalen, RS: URI, 2010.

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Congresso, Latino-Americano de Estratégia (8th 1995 São Leopoldo Rio Grande do Sul Brazil). Anais. São Leopoldo, RS, Brasil: Editora Unisinos, 1996.

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Book chapters on the topic "ANFIS"

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Srivastava, Swatika, Mohd Shariq Ansari, Tanu Dhusia, Ravi Jaiswal, and Poonam Yadav. "Hourly solar irradiation forecasting utilising ANFIS and simulated annealing ANFIS." In Emerging Trends in IoT and Computing Technologies, 52–58. London: Routledge, 2023. http://dx.doi.org/10.1201/9781003350057-9.

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van Oostendorp, Ben, Eric Zander, and Barnabas Bede. "Deep Learning ANFIS Architectures." In Fuzzy Information Processing 2023, 141–48. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46778-3_13.

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Mendez, Gerardo M., and Ma Angeles Hernandez. "Interval Type-2 ANFIS." In Advances in Soft Computing, 64–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74972-1_10.

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Malack, Oteri, Kibet Philip, and Kihato Peter. "Comparing the Performance of ANFIS, LOG10-ANFIS and LOG10-PSO-ANFIS for Universal Theoretical Wireless Signal Propagation Prediction Modelling." In Lecture Notes in Electrical Engineering, 191–206. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1645-8_20.

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Suparta, Wayan, and Kemal Maulana Alhasa. "Estimation of ZTD Using ANFIS." In SpringerBriefs in Meteorology, 53–84. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28437-8_4.

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Barhatte, Alka, Manisha Dale, and Rajesh Ghongade. "ANFIS-Based Cardiac Arrhythmia Classification." In Advances in Deep Learning for Medical Image Analysis, 1–18. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003230540-1.

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Dalecky, Stepan, and Frantisek V. Zboril. "An Approach to ANFIS Performance." In Advances in Intelligent Systems and Computing, 195–206. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19824-8_16.

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Zhang, Rijun, Caishui Hou, Hui Lin, Meiyan Zhuo, Meixin Zhang, Zhongsheng Li, Liwu Sun, and Fengqin Lin. "Application of the Wavelet-ANFIS Model." In Lecture Notes in Electrical Engineering, 1373–79. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01766-2_156.

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Nagothu, Sudheer Kumar. "ANFIS Based Smart Wound Monitoring System." In Communications in Computer and Information Science, 197–208. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5048-2_15.

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Mellah, Rabah, and Redouane Toumi. "Control Bilateral Teleoperation By Compensatory ANFIS." In Advanced Mechatronics Solutions, 167–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23923-1_25.

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Conference papers on the topic "ANFIS"

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Olatunji, Obafemi, Stephen Akinlabi, Nkosinathi Madushele, Paul Adedeji, and Samuel Fatoba. "Comparative Analysis of the Heating Values of Biomass Based on GA-ANFIS and PSO-ANFIS Models." In ASME 2019 Power Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/power2019-1825.

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Abstract This article applied a hybridized, adaptive neuro-fuzzy inference system ANFIS-genetic algorithm (GA-ANFIS) and ANFIS -Particle swarm optimization (PSO-ANFIS) to predict the HHV of biomass. The minimum input parameter for the prediction model is based on the proximate values of biomass which are fixed carbon (FC), ash content (A) and volatile matter (VM). The 214 data which cover a wide range of biomass classes were extracted from reliable literature for the training and testing of the models. The optimal results obtained based on each modelling algorithm were compared. The proposed algorithms were evaluated by statistical indices which are the Coefficient of Correlation (CC), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) estimated at 0.9189, 1.2369,7.4575 and 1.3560 respectively for PSO-ANFIS and 0.9088, 1.1200, 6.3960, 0.8895 respectively for GA-ANFIS. The GA showed exceptional ability to generalize in term of MAPE though at the expense of lesser CC which is obtained in the case of PSO. The reported indices showed that PSO-ANFIS and GA-ANFIS could be applied as an approach to the prediction of HHV based on proximate analysis instead of lengthy experiment procedures.
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Balonji, Serge, Imhade Princess Okokpujie, and Lagouge Tartibu. "Parametric Analysis of ANFIS, ANFIS-PSO, and ANFIS-GA Models for the Prediction of Aluminum Surface Roughness in End-Milling Operation." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95418.

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Abstract Milling is one of the old and common cutting processes that utilize rotating tools to take materials off the main component with a combination of tools and workpiece movements. The texture of a machined surface is a key factor in defining how an essential component interacts with its environment. Trial-and-error machining to produce high-quality surfaces has been a time-consuming method that yields lower production and poor revenue. In this paper, the performances of an Adaptive Network-based Fuzzy Inference System (ANFIS) model has been employed for the prediction of the surface roughness (SR) of a block of Aluminum alloy AI6061 machined on an end-mill CNC machine by varying four input settings namely: The spindle speed of rotation, the tool cutting rate, the radial depth, and the axial depth. The approach consisted of a parametric analysis carried out within each system to obtain the finest models for the prediction. The hybrids ANFIS-PSO and ANFIS-GA have been employed to find out which one, either PSO or GA, optimizes better ANFIS for the prediction of Al6061 SR. Their performances produced better results than the stand-alone ANFIS, with ANFIS-GA yielding the best results of the most negligible RMSE value of 0.01097 and the regression values of 0.9939 for training and 0.8102 for testing.
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Jagtap, Pushpak, and G. N. Pillai. "Comparison of extreme-ANFIS and ANFIS networks for regression problems." In 2014 IEEE International Advance Computing Conference (IACC). IEEE, 2014. http://dx.doi.org/10.1109/iadcc.2014.6779496.

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Abd Elwahed, Amr, Hassan Metered, and Hany Monieb. "Identification of the Nonlinear Dynamic Behavior of Magnetorheological Fluid Dampers using Adaptive Neuro-Fuzzy Inference System." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0123.

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<div class="section abstract"><div class="htmlview paragraph">Adaptive neuro-fuzzy inference system (ANFIS) technique has been developed and applied by numerous researchers as a very useful predictor for nonlinear systems. In this paper, non-parametric models have been investigated to predict the direct and inverse nonlinear dynamic behavior of magnetorheological (MR) fluid dampers using ANFIS technique to demonstrate more accurate and efficient models. The direct ANFIS model can be used to predict the damping force of the MR fluid damper and the inverse dynamic ANFIS model can be used to offer a suitable command voltage applied to the damper coil. The architectures and the learning details of the direct and inverse ANFIS models for MR fluid dampers are introduced and simulation results are discussed. The suggested ANFIS models are used to predict the damping force of the MR fluid damper accurately and precisely. Moreover, validation results for the ANFIS models are proposed and used to evaluate their performance. Validation results with several data sets indicate that the proposed direct and inverse identification models using ANFIS can be used to predict the nonlinear dynamic performance of MR fluid dampers accurately and can work as a damper controller.</div></div>
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Mendez, Gerardo M., and M. de los Angeles Hernandez. "IT2 TSK NSFLS2 ANFIS." In 2010 Ninth Mexican International Conference on Artificial Intelligence (MICAI). IEEE, 2010. http://dx.doi.org/10.1109/micai.2010.9.

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Emam, A., H. Tonekabonipour, M. Teshnelab, and M. Aliyari Shoorehdeli. "Ischemia prediction using ANFIS." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5642197.

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Ginarsa, I. M., A. Soeprijanto, and M. H. Purnomo. "Controlling chaos using ANFIS-based Composite Controller (ANFIS-CC) in power systems." In 2009 International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME). IEEE, 2009. http://dx.doi.org/10.1109/icici-bme.2009.5417262.

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Samanta, B. "Machine Fault Detection Using Neuro-Fuzzy Inference System and Genetic Algorithms." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84643.

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A study is presented to show the performance of machine fault detection using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs), termed here as GA-ANFIS. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GA-ANFIS for two class (normal or fault) recognition. The number and the parameters of membership functions used in ANFIS along with the features are selected using GAs maximizing the classification success. The results of fault detection are compared with GA based artificial neural network (ANN), termed here as GA-ANN. In GA-ANN, the number of hidden nodes and the selection of input features are optimized using GAs. For each trial, the GA-ANFIS and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GA-ANFIS and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers (ANFIS and ANN) with GA based selection of features and classifier parameters.
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Ling, T. G., M. F. Rahmat, and A. R. Husain. "ANFIS modeling and Direct ANFIS Inverse control of an Electro-Hydraulic Actuator system." In 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA 2013). IEEE, 2013. http://dx.doi.org/10.1109/iciea.2013.6566397.

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Sheng, Shuangwen, and Robert X. Gao. "Architectural Effect on ANFIS for Machine Condition Assessment." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60071.

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This paper investigates the architectural effect on adaptive neuro-fuzzy inference system (ANFIS) for machine condition assessment. The study was motivated by ANFIS’s limitation in adapting its architecture to map the modeled input output relationship. Based on the grid input space partition method, two elements in defining an ANFIS architecture were studied: the type of the membership function (MF) and the MF number assigned to ANFIS inputs. A new modeling accuracy index was introduced to address the limitation of the traditional root mean square error (RMSE) in describing the effect of the MF type. The analysis showed that wide core membership functions enabled a smaller RMSE than narrow core membership functions for machine defect severity classification. It is further shown that selecting appropriate MF number is critical to ensuring accuracy of ANFIS, considering the overfitting problem. These results were experimentally investigated on a bearing test bed, where defect severity classification and dynamic load estimation were evaluated. The experiments agreed well with the theoretical analysis.
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Reports on the topic "ANFIS"

1

McLean, Bill, Samuel Shannon, Joe McEntire, and Scott Armstrong. Counterweights Used with ANVIS. Fort Belvoir, VA: Defense Technical Information Center, July 1996. http://dx.doi.org/10.21236/ada311728.

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Peinado-Vara, Estrella, and Antonio Vives. Responsabilidade social da empresa: Um bom negócio para todos. Inter-American Development Bank, December 2006. http://dx.doi.org/10.18235/0007874.

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Estes anais apresentam um resumo das sessões e discussões ocorridas durante a IV Conferência Interamericana sobre Responsabilidade Social da Empresa: Um Bom Negócio para Todos, realizada em Salvador, Bahia, Brasil, de 10 a 12 de dezembro de 2006. A maior parte das apresentações realizadas durante o evento, bem como os anais e as apresentações de edições anteriores pode ser encontrada em www.csramericas.org.
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McLean, William E. ANVIS Objective Lens Depth of Field. Fort Belvoir, VA: Defense Technical Information Center, March 1996. http://dx.doi.org/10.21236/ada306571.

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Towns, Deborah R., and William E. McLean. ANVIS Compatibility with HGU-56/P Helmet. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada300599.

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Lakaszow. KC-130 ANVIS/HUD Assessment and Symbology Rationale. Fort Belvoir, VA: Defense Technical Information Center, February 1994. http://dx.doi.org/10.21236/ada284155.

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Mclean, William E., and Crina van de Pol. Diopter Focus of ANVIS Eyepieces Using Monocular and Binocular Techniques. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada400108.

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King, James M., and Stephen E. Morse. Interpupillary and Vertex Distance Effects on Field-of-View and Acuity With ANVIS. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada261259.

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Gleason, Gerald A., and Joseph T. Reigler. The Effect of Eyepiece Focus on Visual Acuity Through ANVIS Night Vision Goggles During Short - and Long-Term Wear. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada388987.

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