Literatura académica sobre el tema "NEURO-FUZZY ANFIS"
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Artículos de revistas sobre el tema "NEURO-FUZZY ANFIS"
Yeom, Chan-Uk y Keun-Chang Kwak. "Performance Comparison of ANFIS Models by Input Space Partitioning Methods". Symmetry 10, n.º 12 (3 de diciembre de 2018): 700. http://dx.doi.org/10.3390/sym10120700.
Texto completoYeom, Chan-Uk y 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, n.º 23 (27 de noviembre de 2020): 8495. http://dx.doi.org/10.3390/app10238495.
Texto completoSadeghi-Niaraki, Abolghasem, Ozgur Kisi y Soo-Mi Choi. "Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods". PeerJ 8 (14 de agosto de 2020): e8882. http://dx.doi.org/10.7717/peerj.8882.
Texto completoBlahová, Lenka, Ján Dvoran y Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies". Archives of Control Sciences 22, n.º 2 (1 de enero de 2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.
Texto completoBadvaji, Bhumika, Raunak Jangid y 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, n.º 06 (25 de junio de 2022): 14–20. http://dx.doi.org/10.30780/ijtrs.v07.i06.003.
Texto completoTahour, Ahmed, Hamza Abid y Ghani Aissaoui. "Adaptive neuro-fuzzy controller of switched reluctance motor". Serbian Journal of Electrical Engineering 4, n.º 1 (2007): 23–34. http://dx.doi.org/10.2298/sjee0701023t.
Texto completoSangeetha, J. y P. Renuga. "Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices". Journal of Circuits, Systems and Computers 26, n.º 02 (3 de noviembre de 2016): 1750034. http://dx.doi.org/10.1142/s0218126617500347.
Texto completoMindit Eriyadi, S.Pd, M.T. "PERANCANGAN DAN SIMULASI BASIC ENGINE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)". TEMATIK 2, n.º 2 (30 de diciembre de 2015): 105–13. http://dx.doi.org/10.38204/tematik.v2i2.76.
Texto completoKarthikeyan, R., K. Manickavasagam, Shikha Tripathi y K. V. V. Murthy. "Neuro-Fuzzy-Based Control for Parallel Cascade Control". Chemical Product and Process Modeling 8, n.º 1 (8 de junio de 2013): 15–25. http://dx.doi.org/10.1515/cppm-2013-0002.
Texto completoSabet, Masumeh, Mehdi Naseri y Hosein Sabet. "Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System". Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation 42, n.º 1 (1 de enero de 2010): 159–67. http://dx.doi.org/10.2478/v10060-008-0074-6.
Texto completoTesis sobre el tema "NEURO-FUZZY ANFIS"
Dalecký, Štěpán. "Neuro-fuzzy systémy". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236066.
Texto completoHamdan, 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.
Texto completoGuner, 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.
Texto completoFunsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System". DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.
Texto completoLima, 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/.
Texto completoThis 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.
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.
Texto completoCoordena??o de Aperfei?oamento de Pessoal de N?vel Superior
This paper presents a new multi-model technique of dentification in ANFIS for nonlinear systems. In this technique, the structure used is of the fuzzy Takagi-Sugeno of which the consequences are local linear models that represent the system of different points of operation and the precursors are membership functions whose adjustments are realized by the learning phase of the neuro-fuzzy ANFIS technique. The models that represent the system at different points of the operation can be found with linearization techniques like, for example, the Least Squares method that is robust against sounds and of simple application. The fuzzy system is responsible for informing the proportion of each model that should be utilized, using the membership functions. The membership functions can be adjusted by ANFIS with the use of neural network algorithms, like the back propagation error type, in such a way that the models found for each area are correctly interpolated and define an action of each model for possible entries into the system. In multi-models, the definition of action of models is known as metrics and, since this paper is based on ANFIS, it shall be denominated in ANFIS metrics. This way, ANFIS metrics is utilized to interpolate various models, composing a system to be identified. Differing from the traditional ANFIS, the created technique necessarily represents the system in various well defined regions by unaltered models whose pondered activation as per the membership functions. The selection of regions for the application of the Least Squares method is realized manually from the graphic analysis of the system behavior or from the physical characteristics of the plant. This selection serves as a base to initiate the linear model defining technique and generating the initial configuration of the membership functions. The experiments are conducted in a teaching tank, with multiple sections, designed and created to show the characteristics of the technique. The results from this tank illustrate the performance reached by the technique in task of identifying, utilizing configurations of ANFIS, comparing the developed technique with various models of simple metrics and comparing with the NNARX technique, also adapted to identification
Este trabalho apresenta uma nova t?cnica de identifica??o multimodelos baseada em ANFIS para sistemas n?o lineares. Nesta t?cnica, a estrutura utilizada ? do tipo fuzzy Takagi-Sugeno cujos consequentes s?o modelos lineares locais que representam o sistema em diferentes pontos de opera??o e os antecedentes s?o fun??es de pertin?ncia cujos ajustes s?o realizados pela fase de aprendizagem da t?cnica neuro-fuzzy ANFIS. Modelos que representem o sistema em diferentes pontos de opera??o podem ser encontrados com t?cnicas de lineariza??o como, por exemplo, o m?todo dos M?nimos Quadrados que ? robusto a ru?dos e de simples aplica??o. Cabe ? fase de implica??o do sistema fuzzy informar a propor??o de cada modelo que deve ser empregada, utilizando, para isto, as fun??es de pertin?ncia. As fun??es de pertin?ncia podem ser ajustadas pelo ANFIS com o uso de algoritmos de redes neurais, como o de retropropaga??o do erro, de modo que os modelos encontrados para cada regi?o sejam devidamente interpolados e, assim, definam-se a atua??o de cada modelo para as poss?veis entradas do sistema. Em multimodelos a defini??o de atua??o de modelos ? conhecida por m?trica e, como neste trabalho ? realizada pelo ANFIS, ser? denominada de m?trica ANFIS. Desta forma, uma m?trica ANFIS ? utilizada para interpolar v?rios modelos, compondo o sistema a ser identificado. Diferentemente do ANFIS tradicional, a t?cnica desenvolvida necessariamente representa o sistema em v?rias regi?es bem definidas por modelos inalter?veis que, por sua vez, ter?o sua ativa??o ponderada a partir das fun??es de pertin?ncia. A sele??o de regi?es para a aplica??o do m?todo dos M?nimos Quadrados ? realizada manualmente a partir da an?lise gr?fica do comportamento do sistema ou a partir do conhecimento de caracter?sticas f?sicas da planta. Esta sele??o serve como base para iniciar a t?cnica definindo modelos lineares e gerando a configura??o inicial das fun??es de pertin?ncia. Experimentos s?o realizados em um tanque did?tico, com m?ltiplas se??es, projetado e desenvolvido com a finalidade de mostrar caracter?sticas da t?cnica. Os resultados neste tanque ilustram o bom desempenho alcan?ado pela t?cnica na tarefa de identifica??o, utilizando, para isto, v?rias configura??es do ANFIS, comparando a t?cnica desenvolvida com m?ltiplos modelos de m?trica simples e comparando com a t?cnica NNARX, tamb?m adaptada para identifica??o
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.
Texto completoJain, 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.
Texto completoAslan, 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.
Texto completoMatlab 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.
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.
Texto completoResumo: 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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Libros sobre el tema "NEURO-FUZZY ANFIS"
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.
Buscar texto completoCapítulos de libros sobre el tema "NEURO-FUZZY ANFIS"
Maurya, Akhilesh Kumar y Devesh Kumar Patel. "Vehicle Classification Using Adaptive Neuro-Fuzzy Inference System (ANFIS)". En 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.
Texto completoMaurya, Akhilesh Kumar y Devesh Kumar Patel. "Vehicle Classification Using Adaptive Neuro Fuzzy Inference System (ANFIS)". En Advances in Intelligent Systems and Computing, E1. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2220-0_54.
Texto completoAydin, Olgun y Elvan Aktürk Hayat. "Estimation of Housing Demand with Adaptive Neuro-Fuzzy Inference Systems (ANFIS)". En 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.
Texto completoIgamberdiev, H. Z., A. N. Yusupbekov, U. F. Mamirov y Sh D. Tulyaganov. "Regular Identification Algorithms for a Special Class of Neuro-Fuzzy Models ANFIS". En 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.
Texto completoFaycal, Djebbas, Zeddouri Aziez y Belila Djilani. "Prediction of the Porosity Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Technique". En 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.
Texto completoZilouchian, Ali, David W. Howard y Timothy Jordanides. "An Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to control of robotic manipulators". En 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.
Texto completoAdedeji, P. A., S. O. Masebinu, S. A. Akinlabi y N. Madushele. "Adaptive Neuro-fuzzy Inference System (ANFIS) Modelling in Energy System and Water Resources". En 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.
Texto completoVyas, Megha, Vinod Kumar, Shripati Vyas y Raju Kumar Swami. "Grid-Connected DFIG-Based Wind Energy Conversion System with ANFIS Neuro-Fuzzy Controller". En Lecture Notes in Electrical Engineering, 601–12. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4975-3_48.
Texto completoHakim, Seyed Jamalaldin S. y H. Abdul Razak. "Damage Identification Using Experimental Modal Analysis and Adaptive Neuro-Fuzzy Interface System (ANFIS)". En 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.
Texto completoAdeleke, Oluwatobi, Stephen A. Akinlabi, Paul A. Adedeji y Tien-Chien Jen. "Energy Content Modelling for Municipal Solid Waste Using Adaptive Neuro-Fuzzy Inference System (ANFIS)". En Lecture Notes in Mechanical Engineering, 177–85. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5753-8_17.
Texto completoActas de conferencias sobre el tema "NEURO-FUZZY ANFIS"
Mehrabi, Mehdi, Mohsen Sharifpur y Josua P. Meyer. "Adaptive Neuro-Fuzzy Modeling of the Thermal Conductivity of Alumina-Water Nanofluids". En 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.
Texto completoSmaili, Ahmad, Fouad Mrad y Hadi Maamoun. "Neuro-Fuzzy Control of Smart Flexible Mechanisms". En 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.
Texto completoSamanta, B. "Machine Fault Detection Using Neuro-Fuzzy Inference System and Genetic Algorithms". En ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84643.
Texto completoMota, Vania y Daniel Leite. "Sistema de Inferência Neuro-Fuzzy para Análise Microbiológica de Processos de Compostagem". En Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1435.
Texto completoBettocchi, R., M. Pinelli, P. R. Spina y M. Venturini. "Artificial Intelligence for the Diagnostics of Gas Turbines: Part II — Neuro-Fuzzy Approach". En ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68027.
Texto completoMohd. Hashim, S. Z. y M. O. Tokhi. "ANFIS Active Vibration Control of Flexible Beam Structures". En ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58204.
Texto completoLiu, Shi y Liangsheng Qu. "Application of Adaptive Neuro-Fuzzy Inference System in Field Balancing". En ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80367.
Texto completoAlbakkar, A. y O. P. Malik. "Adaptive neuro-fuzzy controller based on simplified ANFIS network". En 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6344842.
Texto completoAbd Elwahed, Amr, Hassan Metered y Hany Monieb. "Identification of the Nonlinear Dynamic Behavior of Magnetorheological Fluid Dampers using Adaptive Neuro-Fuzzy Inference System". En WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0123.
Texto completoDelavari, Ehsan, Ahmad Reza Mostafa Gharabaghi y Mohammad Reza Chenaghlou. "Prediction of Water Wave Breaking Height and Depth Using ANFIS". En ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49825.
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