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Journal articles on the topic "NEURO-FUZZY 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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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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Sadeghi-Niaraki, Abolghasem, Ozgur Kisi, and Soo-Mi Choi. "Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods." PeerJ 8 (August 14, 2020): e8882. http://dx.doi.org/10.7717/peerj.8882.
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This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.
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Blahová, Lenka, Ján Dvoran, and Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies." Archives of Control Sciences 22, no. 2 (January 1, 2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.
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Neuro-fuzzy control design of processes in chemical technologies The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and laboratory mixing process.
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Badvaji, Bhumika, Raunak Jangid, and Kapil Parikh. "PERFORMANCE ANALYSIS ON ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) BASED MPPT CONTROLLER FOR DC-DC CONVERTER FOR STANDALONE SOLAR ENERGY GENERATION SYSTEM." International Journal of Technical Research & Science 7, no. 06 (June 25, 2022): 14–20. http://dx.doi.org/10.30780/ijtrs.v07.i06.003.
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This paper presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller for a DC to DC converter. The proposed system consists of 2.0 kW PV array, DC to DC boost converter and load. The proposed algorithm has advantages of neural and fuzzy networks. To enhance of converter performance, Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller is used. In order to demonstrate the proposed ANFIS controller abilities to follow the reference voltage and current, its performance is simulated and compared with Artificial Intelligence Technique based MPPT controller. Simulation results show that for a wide range of input irradiance, Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller shows improved performance than the Artificial Intelligence Technique based MPPT controller with at various operating conditions.
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Tahour, Ahmed, Hamza Abid, and Ghani Aissaoui. "Adaptive neuro-fuzzy controller of switched reluctance motor." Serbian Journal of Electrical Engineering 4, no. 1 (2007): 23–34. http://dx.doi.org/10.2298/sjee0701023t.
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This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI).
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Sangeetha, J., and P. Renuga. "Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices." Journal of Circuits, Systems and Computers 26, no. 02 (November 3, 2016): 1750034. http://dx.doi.org/10.1142/s0218126617500347.
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This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature.
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Mindit Eriyadi, S.Pd, M.T. "PERANCANGAN DAN SIMULASI BASIC ENGINE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)." TEMATIK 2, no. 2 (December 30, 2015): 105–13. http://dx.doi.org/10.38204/tematik.v2i2.76.
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Abstrak : Adaptif Neuro Fuzzy Inference System ( ANFIS ) merupakan salah satu variasi bentuk dari fuzzy. Untuk dapat menggunakan ANFIS, dapat dibuat engine ANFIS yang berfungsi menjalankan logika fuzzy yang dirancang. Perancangan dan simulasi basic engine ANFIS ini bertujuan untuk merancang sebuah basic engine ANFIS dan menguji performansinya dalam sebuah simulasi. Perancangan dan pengujian simulasi dilakukan dengan menggunakan perangkat lunak MATLAB 7.5.0 dengan fitur anfis editor. Dari hasil pengujian simulasi basic engine ANFIS yang dirancang, didapatkan hasil bahwa basic engine yang dirancang dapat menghasikan sebuah keputusan yang tepat dengan menggunakan input – input yang ada.
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Karthikeyan, R., K. Manickavasagam, Shikha Tripathi, and K. V. V. Murthy. "Neuro-Fuzzy-Based Control for Parallel Cascade Control." Chemical Product and Process Modeling 8, no. 1 (June 8, 2013): 15–25. http://dx.doi.org/10.1515/cppm-2013-0002.
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Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.
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Sabet, Masumeh, Mehdi Naseri, and Hosein Sabet. "Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System." Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation 42, no. 1 (January 1, 2010): 159–67. http://dx.doi.org/10.2478/v10060-008-0074-6.
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Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past, an accurate and reliable estimation of the rate of sand drift has still remained a problem. It is a non-linear process and can be described by chaotic time-series. The current study addresses this issue through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is about taking an initial fuzzy inference system (FIS) and tuning it with a back propagation algorithm based on the collection of input-output data. ANFIS was developed to predict the sand drift from a variety of causative variables. The structure and algorithm of ANFIS for predicting the rate of sand drift is described. The Adaptive Neuro-Fuzzy Inference System was validated by confirming its consistency with a database of specified physical process.
Dissertations / Theses on the topic "NEURO-FUZZY ANFIS"
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Dalecký, Štěpán. "Neuro-fuzzy systémy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236066.
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The thesis deals with artificial neural networks theory. Subsequently, fuzzy sets are being described and fuzzy logic is explained. The hybrid neuro-fuzzy system stemming from ANFIS system is designed on the basis of artificial neural networks, fuzzy sets and fuzzy logic. The upper-mentioned systems' functionality has been demonstrated on an inverted pendulum controlling problem. The three controllers have been designed for the controlling needs - the first one is on the basis of artificial neural networks, the second is a fuzzy one, and the third is based on ANFIS system. The thesis is aimed at comparing the described systems, which the controllers have been designed on the basis of, and evaluating the hybrid neuro-fuzzy system ANFIS contribution in comparison with particular theory solutions. Finally, some experiments with the systems are demonstrated and findings are assessed.
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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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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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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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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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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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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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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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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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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.
Full textAbstract:
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 &ldquoMatlab 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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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.
Full textAbstract:
Orientador: Suely Cunha Amaro MantovaniResumo: 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)
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Books on the topic "NEURO-FUZZY ANFIS"
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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.
Find full textBook chapters on the topic "NEURO-FUZZY ANFIS"
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Maurya, Akhilesh Kumar, and Devesh Kumar Patel. "Vehicle Classification Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." In Advances in Intelligent Systems and Computing, 137–52. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2220-0_11.
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Maurya, Akhilesh Kumar, and Devesh Kumar Patel. "Vehicle Classification Using Adaptive Neuro Fuzzy Inference System (ANFIS)." In Advances in Intelligent Systems and Computing, E1. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2220-0_54.
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Aydin, Olgun, and Elvan Aktürk Hayat. "Estimation of Housing Demand with Adaptive Neuro-Fuzzy Inference Systems (ANFIS)." In The Impact of Globalization on International Finance and Accounting, 449–55. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68762-9_49.
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Igamberdiev, H. Z., A. N. Yusupbekov, U. F. Mamirov, and Sh D. Tulyaganov. "Regular Identification Algorithms for a Special Class of Neuro-Fuzzy Models ANFIS." In Lecture Notes in Networks and Systems, 747–53. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25252-5_97.
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Faycal, Djebbas, Zeddouri Aziez, and Belila Djilani. "Prediction of the Porosity Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Technique." In Springer Series in Geomechanics and Geoengineering, 995–1011. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1964-2_87.
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Zilouchian, Ali, David W. Howard, and Timothy Jordanides. "An Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to control of robotic manipulators." In Tasks and Methods in Applied Artificial Intelligence, 383–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64574-8_424.
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Adedeji, P. A., S. O. Masebinu, S. A. Akinlabi, and N. Madushele. "Adaptive Neuro-fuzzy Inference System (ANFIS) Modelling in Energy System and Water Resources." In Optimization Using Evolutionary Algorithms and Metaheuristics, 117–33. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, 2019. | Series: Science, technology, and management series: CRC Press, 2019. http://dx.doi.org/10.1201/9780429293030-7.
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Vyas, Megha, Vinod Kumar, Shripati Vyas, and Raju Kumar Swami. "Grid-Connected DFIG-Based Wind Energy Conversion System with ANFIS Neuro-Fuzzy Controller." In Lecture Notes in Electrical Engineering, 601–12. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4975-3_48.
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Hakim, Seyed Jamalaldin S., and H. Abdul Razak. "Damage Identification Using Experimental Modal Analysis and Adaptive Neuro-Fuzzy Interface System (ANFIS)." In Topics in Modal Analysis I, Volume 5, 399–405. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2425-3_37.
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Adeleke, Oluwatobi, Stephen A. Akinlabi, Paul A. Adedeji, and Tien-Chien Jen. "Energy Content Modelling for Municipal Solid Waste Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." In Lecture Notes in Mechanical Engineering, 177–85. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5753-8_17.
Full textConference papers on the topic "NEURO-FUZZY ANFIS"
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Mehrabi, Mehdi, Mohsen Sharifpur, and Josua P. Meyer. "Adaptive Neuro-Fuzzy Modeling of the Thermal Conductivity of Alumina-Water Nanofluids." In ASME 2012 Third International Conference on Micro/Nanoscale Heat and Mass Transfer. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/mnhmt2012-75023.
Full textAbstract:
By using on Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as experimental data, a model was established for the prediction of the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In the ANFIS the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al2O3 nanoparticle size were considered as the input (design) parameters. In the development of the model, the empirical data was divided into train and test sections. The ANFIS network was instructed by eighty percent of the experimental data and the remaining data (twenty percent) were considered for benchmarking. The results which were obtained by the proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) model were in good agreement with the experimental results.
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Smaili, Ahmad, Fouad Mrad, and Hadi Maamoun. "Neuro-Fuzzy Control of Smart Flexible Mechanisms." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/vib-48366.
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This paper presents an analytical investigation in which a controller based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is designed and implemented to control the vibrations of a flexible mechanism system with smart coupler link. The most dominant vibration mode of the mechanism is identified and the controller is then designed to reduce the effect of this mode on the response of the mechanism system. The proposed control algorithm is implemented on a mechanism system with a thin plate-type piezoceramic actuator bonded to the coupler link surface at the high strain location corresponding to the dominant mode. Simulation results showed the capability of the proposed ANFIS controller to reduce the coupler link mid point amplitudes by nearly 60%.
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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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Mota, Vania, and Daniel Leite. "Sistema de Inferência Neuro-Fuzzy para Análise Microbiológica de Processos de Compostagem." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1435.
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A cama de galpões de confinamento de bovino leiteiro no modelo compost barn tem grande impacto na qualidade e produtividade animal. O objetivo deste estudo é desenvolver um modelo não-linear para estimar a quantidade de bactéria em camas de compostagem. A partir de variáveis de fácil mensuração, estimativas podem ser geradas pelo modelo e, consequentemente, análises laboratoriais caras e demoradas são evitadas. Um modelo de inferência neuro-fuzzy, ANFIS, é considerado. A pesquisa foi realizada em uma propriedade em Três Corações. Diferentes funções de pertinência fuzzy e algoritmos de aprendizado, tais como Fuzzy C-Means e Retro-Propagação de Erro, foram avaliados na construção do modelo ANFIS. Os resultados mostram que ANFIS prediz a contagem total de bactérias com boa acurácia. A melhor configuração ANFIS usa cinco atributos facilmente mensuráveis (pH, umidade, temperatura na superfície e a 0,15m de profundidade, e resíduo mineral fixo); funções de pertinência Gaussianas apropriadamente posicionadas no espaço dos dados via Fuzzy C-Means; e o algoritmo de retro-propagação para adaptar os coeficientes de funções consequentes afins. Ademais, um modelo simplificado com quatro atributos - excluído o resíduo mineral fixo - usando função de pertinência sino generalizado é sugerido alternativamente ao preço de uma pequena redução em acurácia.
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Bettocchi, R., M. Pinelli, P. R. Spina, and M. Venturini. "Artificial Intelligence for the Diagnostics of Gas Turbines: Part II — Neuro-Fuzzy Approach." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68027.
Full textAbstract:
In the paper, Neuro-Fuzzy Systems (NFSs) for gas turbine diagnostics are studied and developed. The same procedure used previously for the set up of Neural Network (NN) models was used. In particular, the same database of patterns was used for both training and testing the NFSs. This database was obtained by running a Cycle Program, calibrated on a 255 MW single shaft gas turbine working in the ENEL combined cycle power plant of La Spezia (Italy). The database contains the variations of the Health Indices (which are the characteristic parameters that are indices of gas turbine health state, such as efficiencies and characteristic flow passage areas of compressor and turbine) and the corresponding variations of the measured quantities with respect to the values in new and clean conditions. The analyses carried out are aimed at the selection of the most appropriate NFS structure for gas turbine diagnostics, in terms of computational time of the NFS training phase, accuracy and robustness towards measurement uncertainty during simulations. In particular, Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures were considered and tested, and their performance was compared to that obtainable by using the NN models. An analysis was also performed in order to identify the most significant ANFIS inputs. The results obtained show that ANFISs are robust with respect to measurement uncertainty, and, in all the cases analyzed, the performance (in terms of accuracy during simulations and time spent for the training phase) proved to be better than that obtainable by MIMO and MISO Neural Networks trained and tested on the same data.
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Mohd. Hashim, S. Z., and M. O. Tokhi. "ANFIS Active Vibration Control of Flexible Beam Structures." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58204.
Full textAbstract:
This paper presents the development of an adaptive neuro-fuzzy inference system (ANFIS) controller for vibration control of flexible beam structures. ANFIS constructs a fuzzy inference system (FIS) whose membership function parameters are tuned (adjusted) using the backpropagation algorithm and least squares method. This allows the fuzzy system to learn from the data modeling. To allow the non-linear dynamics of the system be incorporated within the design, a pseudo random binary signal (PRBS) covering the dynamic range of interest of the system is used to train the ANFIS model, which gives good output prediction. Simulation results showing the performance of the developed control scheme in vibration suppression of flexible beam structures, with changes in the excitation signal, are presented and discussed.
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Liu, Shi, and Liangsheng Qu. "Application of Adaptive Neuro-Fuzzy Inference System in Field Balancing." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80367.
Full textAbstract:
The field balancing of flexible rotors is one of the key techniques to reduce vibration of large rotating machinery. Although in recent decades the balancing theory has been thoroughly studied and various balancing techniques have been well developed, the present balancing methods are still remain for further improvements in accuracy and efficiency. Firstly, most balancing methods need large numbers of trial runs to obtain the vibration responses of trial weights in different correcting planes. Secondly, the vibration response in each measured section is always taken from a single sensor, and thus are lack of comprehensive vibration information of rotor. In fact, the movement of rotor is a complex spatial motion, which can’t be objectively and reliably described just with a single sensor in each bearing section. In order to overcome above shortcomings of traditional balancing methods, this paper presents a new field balancing method for flexible rotors, which is based on adaptive neuro-fuzzy inference system (ANFIS). The new method successfully applies the information fusion, ANFIS and computer simulation together. It integrates and fully utilizes the information supplied from all proximity sensors by holospectrum for enhancing the balancing efficiency and accuracy. A fuzzy model is established to simulate the mapping relationship between vibration responses and balancing weights by using the ANFIS. The inputs into ANFIS are the amplitudes and phases of integrated vibration responses, while the outputs are the mass and azimuth of balancing weights. A fuzzy set with three membership functions (MFs) is used to describe the magnitude of vibration amplitudes or of balancing weights. Another fuzzy set with five MFs is used to describe the quadrant of vibration phases or of balancing weights. Based on the historical balancing data, a combination of least-square and back-propagation gradient descent methods is then used for training ANFIS membership function and node-parameters to model input (vibration response)/output (balancing weight) data. The simulation study shows that the ANFIS can obtain satisfactory balancing result after a single trial run. At the same time, with the help of computer simulation, different correction schemes can be compared and rapidly simulated to direct balancing operation. Finally, the effectiveness of the new method was validated by the experiments on balancing rig and in the field balancing practice of several 300MW turbo-generator units.
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Albakkar, A., and O. P. Malik. "Adaptive neuro-fuzzy controller based on simplified ANFIS network." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6344842.
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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.
Full textAbstract:
<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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Delavari, Ehsan, Ahmad Reza Mostafa Gharabaghi, and Mohammad Reza Chenaghlou. "Prediction of Water Wave Breaking Height and Depth Using ANFIS." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49825.
Full textAbstract:
Wave height as well as water depth at the breaking point are two basic parameters which are necessary for studying coastal processes. In this paper, the application of Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) and semi-empirical models are investigated. The data sets used in this study are published laboratory data obtained from regular wave breaking on plane, impermeable slopes collected from 22 sources. Results indicate that the developed ANFIS model provides more accurate and reliable estimation of breaking wave height, compared to semi-empirical equations. However, some of semi-empirical equations provide better predictions of water depth at the breaking point compared to the ANFIS model.