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Artykuły w czasopismach na temat "Fuzzy neural networks"
Rao, D. H. "Fuzzy Neural Networks". IETE Journal of Research 44, nr 4-5 (lipiec 1998): 227–36. http://dx.doi.org/10.1080/03772063.1998.11416049.
Pełny tekst źródłaThakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic". International Journal for Research in Applied Science and Engineering Technology 9, nr 9 (30.09.2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.
Pełny tekst źródłaOH, SUNG-KWUN, DONG-WON KIM i WITOLD PEDRYCZ. "HYBRID FUZZY POLYNOMIAL NEURAL NETWORKS". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, nr 03 (czerwiec 2002): 257–80. http://dx.doi.org/10.1142/s0218488502001478.
Pełny tekst źródłaISHIBUCHI, Hisao, Hidehiko OKADA i Hideo TANAKA. "Fuzzy Neural Networks with Fuzzy Weights". Transactions of the Institute of Systems, Control and Information Engineers 6, nr 3 (1993): 137–48. http://dx.doi.org/10.5687/iscie.6.137.
Pełny tekst źródłaGeng, Z. Jason. "Fuzzy CMAC Neural Networks". Journal of Intelligent and Fuzzy Systems 3, nr 1 (1995): 87–102. http://dx.doi.org/10.3233/ifs-1995-3108.
Pełny tekst źródłaDunyak, James, i Donald Wunsch. "Fuzzy number neural networks". Fuzzy Sets and Systems 108, nr 1 (listopad 1999): 49–58. http://dx.doi.org/10.1016/s0165-0114(97)00339-4.
Pełny tekst źródłaVirgil Negoita, Constantin. "Neural Networks as Fuzzy Systems". Kybernetes 23, nr 3 (1.04.1994): 7–9. http://dx.doi.org/10.1108/03684929410059000.
Pełny tekst źródłaReddy, Bapatu Siva Kumar, i P. Vishnu Vardhan. "Novel Alphabet Deduction Using MATLAB by Neural Networks and Comparison with the Fuzzy Classifier". Alinteri Journal of Agriculture Sciences 36, nr 1 (29.06.2021): 623–28. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21088.
Pełny tekst źródłaPurushothaman, G., i N. B. Karayiannis. "Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks". IEEE Transactions on Neural Networks 8, nr 3 (maj 1997): 679–93. http://dx.doi.org/10.1109/72.572106.
Pełny tekst źródłaBlake, J. "The implementation of fuzzy systems, neural networks and fuzzy neural networks using FPGAs". Information Sciences 112, nr 1-4 (grudzień 1998): 151–68. http://dx.doi.org/10.1016/s0020-0255(98)10029-4.
Pełny tekst źródłaRozprawy doktorskie na temat "Fuzzy neural networks"
Glackin, Cornelius. "Fuzzy spiking neural networks". Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.505831.
Pełny tekst źródłaBrande, Julia K. Jr. "Computer Network Routing with a Fuzzy Neural Network". Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29685.
Pełny tekst źródłaPh. D.
Pirovolou, Dimitrios K. "The tracking problem using fuzzy neural networks". Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14824.
Pełny tekst źródłaFrayman, Yakov, i mikewood@deakin edu au. "Fuzzy neural networks for control of dynamic systems". Deakin University. School of Computing and Mathematics, 1999. http://tux.lib.deakin.edu.au./adt-VDU/public/adt-VDU20051017.145550.
Pełny tekst źródłaLeng, Gang. "Algorithmic developments for self-organising fuzzy neural networks". Thesis, University of Ulster, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405165.
Pełny tekst źródłaRENTERIA, ALEXANDRE ROBERTO. "TRAFFIC CONTROL THROUGH FUZZY LOGIC AND NEURAL NETWORKS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2695@1.
Pełny tekst źródłaEste trabalho apresenta a utilização de lógica fuzzy e de redes neurais no desenvolvimento de um controlador de semáforos - o FUNNCON. O trabalho realizado consiste em quatro etapas principais: estudo dos fundamentos de engenharia de tráfego; definição de uma metodologia para a avaliação de cruzamentos sinalizados; definição do modelo do controlador proposto; e implementação com dados reais em um estudo de caso.O estudo sobre os fundamentos de engenharia de tráfego aborda a definição de termos,os parâmetros utilizados na descrição dos fluxos de tráfego, os tipos de cruzamentos e seus semáforos, os sistemas de controle de tráfego mais utilizados e as diversas medidas de desempenho.Para se efetuar a análise dos resultados do FUNNCON, é definida uma metodologia para a avaliação de controladores. Apresenta-se, também, uma investigação sobre simuladores de tráfego existentes, de modo a permitir a escolha do mais adequado para o presente estudo. A definição do modelo do FUNNCON compreende uma descrição geral dos diversos módulos que o compõem. Em seguida, cada um destes módulos é estudado separadamente: o uso de redes neurais para a predição de tráfego futuro; a elaboração de um banco de cenários ótimos através de um otimizador; e a criação de regras fuzzy a partir deste banco.No estudo de caso, o FUNNCON é implementado com dados reais fornecidos pela CET-Rio em um cruzamento do Rio de Janeiro e comparado com o controlador existente.É constatado que redes neurais são capazes de fornecer bons resultados na predição do tráfego futuro. Também pode ser observado que as regras fuzzy criadas a partir do banco de cenários ótimos proporcionam um controle efetivo do tráfego no cruzamento estudado. Uma comparação entre o desempenho do FUNNCON e o do sistema atualmente em operação é amplamente favorável ao primeiro.
This work presents the use of fuzzy logic and neural networks in the development of a traffic signal controller - FUNNCON. The work consists of four main sections: study of traffic engineering fundamentals; definition of a methodology for evaluation of traffic controls; definition of the proposed controller model; and implementation on a case study using real data.The study of traffic engineering fundamentals considers definitions of terms,parameters used for traffic flow description, types of intersections and their traffic signals,commonly used traffic control systems and performance measures.In order to analyse the results provided by FUNNCON, a methodology for the evaluation of controllers is defined. The existing traffic simulators are investigated, in order to select the best one for the present study.The definition of the FUNNCON model includes a brief description of its modules.Thereafter each module is studied separately: the use of neural networks for future traffic prediction; the setup of a best scenario database using an optimizer; and the extraction of fuzzy rules from this database.In the case study, FUNNCON is implemented with real data supplied by CET-Rio from an intersection in Rio de Janeiro; its performance is compared with that of the existing controller.It can be observed that neural networks can present good results in the prediction of future traffic and that the fuzzy rules created from the best scenario database lead to an effective traffic control at the considered intersection. When compared with the system in operation, FUNNCON reveals itself much superior.
Kim, Hung-man. "Implementing adaptive fuzzy logic controllers with neural networks". Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187160.
Pełny tekst źródłaGabrys, Bogdan. "Neural network based decision support : modelling and simulation of water distribution networks". Thesis, Nottingham Trent University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387534.
Pełny tekst źródłaBordignon, Fernando Luis. "Aprendizado extremo para redes neurais fuzzy baseadas em uninormas". [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259061.
Pełny tekst źródłaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-22T00:50:20Z (GMT). No. of bitstreams: 1 Bordignon_FernandoLuis_M.pdf: 1666872 bytes, checksum: 4d838dfb4ec418698d9ecd3b74e7c981 (MD5) Previous issue date: 2013
Resumo: Sistemas evolutivos são sistemas com alto nível de adaptação capazes de modificar simultaneamente suas estruturas e parâmetros a partir de um fluxo de dados, recursivamente. Aprendizagem a partir de fluxos de dados é um problema contemporâneo e difícil devido à taxa de aumento da dimensão, tamanho e disponibilidade temporal de dados, criando dificuldades para métodos tradicionais de aprendizado. Esta dissertação, além de apresentar uma revisão da literatura de sistemas evolutivos e redes neurais fuzzy, aborda uma estrutura e introduz um método de aprendizagem evolutivo para treinar redes neurais híbridas baseadas em uninormas, usando conceitos de aprendizado extremo. Neurônios baseados em uninormas fundamentados nas normas e conormas triangulares generalizam neurônios fuzzy. Uninormas trazem flexibilidade e generalidade a modelos neurais fuzzy, pois elas podem se comportar como normas triangulares, conormas triangulares, ou de forma intermediária por meio do ajuste de elementos identidade. Este recurso adiciona uma forma de plasticidade em modelos de redes neurais. Um método de agrupamento recursivo para granularizar o espaço de entrada e um esquema baseado no aprendizado extremo compõem um algoritmo para treinar a rede neural. _E provado que uma versão estática da rede neural fuzzy baseada em uninormas aproxima funções contínuas em domínios compactos, ou seja, _e um aproximador universal. Postula-se, e experimentos computacionais endossam, que a rede neural fuzzy evolutiva compartilha capacidade de aproximação equivalente, ou melhor, em ambientes dinâmicos, do que as suas equivalentes estáticas
Abstract: Evolving systems are highly adaptive systems able to simultaneously modify their structures and parameters from a stream of data, online. Learning from data streams is a contemporary and challenging issue due to the increasing rate of the size and temporal availability of data, turning the application of traditional learning methods limited. This dissertation, in addition to reviewing the literature of evolving systems and neuro fuzzy networks, addresses a structure and introduces an evolving learning approach to train uninorm-based hybrid neural networks using extreme learning concepts. Uninorm-based neurons, rooted in triangular norms and conorms, generalize fuzzy neurons. Uninorms bring flexibility and generality to fuzzy neuron models as they can behave like triangular norms, triangular conorms, or in between by adjusting identity elements. This feature adds a form of plasticity in neural network modeling. An incremental clustering method is used to granulate the input space, and a scheme based on extreme learning is developed to train the neural network. It is proved that a static version of the uninorm-based neuro fuzzy network approximate continuous functions in compact domains, i.e. it is a universal approximator. It is postulated and computational experiments endorse, that the evolving neuro fuzzy network share equivalent or better approximation capability in dynamic environments than their static counterparts
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica
Aimejalii, K., Keshav P. Dahal i M. Alamgir Hossain. "GA-based learning algorithms to identify fuzzy rules for fuzzy neural networks". IEEE, 2007. http://hdl.handle.net/10454/2553.
Pełny tekst źródłaKsiążki na temat "Fuzzy neural networks"
Abe, Shigeo. Neural Networks and Fuzzy Systems. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6253-5.
Pełny tekst źródłaRao, Valluru. C++ neural networks and fuzzy logic. Wyd. 2. New York: MIS:Press, 1995.
Znajdź pełny tekst źródłaRao, Valluru. C++ neural networks and fuzzy logic. New York: MIS:Press, 1993.
Znajdź pełny tekst źródłaYager, R. R. Fuzzy sets, neural networks and soft computing. New York: Van Nostrand Reinhold, 1994.
Znajdź pełny tekst źródłaInternational conference (February 12-14, 1996 Lausanne, Switzerland). Microeletronics for neural networks and fuzzy systems. Los Alamitos, Calif: IEEE, 1996.
Znajdź pełny tekst źródłaFuruhashi, Takeshi, i Yoshiki Uchikawa, red. Fuzzy Logic, Neural Networks, and Evolutionary Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7.
Pełny tekst źródła1941-, Yager Ronald R., i Zadeh Lotfi Asker, red. Fuzzy sets, neural networks, and soft computing. New York: Van Nostrand Reinhold, 1994.
Znajdź pełny tekst źródłaFuzzy sets engineering. Boca Raton, Fla: CRC Press, 1995.
Znajdź pełny tekst źródłaRudolf, Kruse, i Klawonn F, red. Foundations of neuro-fuzzy systems. Chichester: John Wiley, 1997.
Znajdź pełny tekst źródłaStavroulakis, Peter. Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.
Znajdź pełny tekst źródłaCzęści książek na temat "Fuzzy neural networks"
Rojas, Raúl. "Fuzzy Logic". W Neural Networks, 287–308. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4_11.
Pełny tekst źródłaCzogała, Ernest, i Jacek Łęski. "Artificial neural networks". W Fuzzy and Neuro-Fuzzy Intelligent Systems, 65–92. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1853-6_3.
Pełny tekst źródłaSingh, Himanshu, i Yunis Ahmad Lone. "Fuzzy Neural Networks". W Deep Neuro-Fuzzy Systems with Python, 199–221. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5361-8_6.
Pełny tekst źródłaFullér, Robert. "Fuzzy neural networks". W Introduction to Neuro-Fuzzy Systems, 171–254. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1852-9_3.
Pełny tekst źródłaPrasad, Nadipuram Ram R. "Neural Networks and Fuzzy Logic". W Fuzzy Systems, 381–401. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5505-6_11.
Pełny tekst źródłaAbe, Shigeo. "Other Neural Networks". W Neural Networks and Fuzzy Systems, 93–125. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6253-5_4.
Pełny tekst źródłaFullér, Robert. "Artificial neural networks". W Introduction to Neuro-Fuzzy Systems, 133–70. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1852-9_2.
Pełny tekst źródłaSingh, Himanshu, i Yunis Ahmad Lone. "Artificial Neural Networks". W Deep Neuro-Fuzzy Systems with Python, 157–98. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5361-8_5.
Pełny tekst źródłaJin, Yaochu. "Artificial Neural Networks". W Advanced Fuzzy Systems Design and Applications, 73–91. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1771-3_3.
Pełny tekst źródłaTsoukalas, L. H., A. Ikonomopoulos i R. E. Uhrig. "Fuzzy neural control". W Artificial Neural Networks for Intelligent Manufacturing, 413–34. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0713-6_15.
Pełny tekst źródłaStreszczenia konferencji na temat "Fuzzy neural networks"
Kumar, Manish, i Devendra P. Garg. "Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters". W ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59589.
Pełny tekst źródłaAouiti, Chaouki, Farah Dridi i Fakhri Karray. "New Results on Neutral Type Fuzzy Based Cellular Neural Networks". W 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491607.
Pełny tekst źródłaJi-Cheng Duan i Fu-Lai Chung. "Cascading fuzzy neural networks". W Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.793206.
Pełny tekst źródłaAmina, Mahdi, i Vassilis S. Kodogiannis. "Load forecasting using fuzzy wavelet neural networks". W 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007492.
Pełny tekst źródłaKowalski, Piotr A., i Tomasz Sloczynski. "Saturation in Fuzzy Flip-Flop Neural Networks". W 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882672.
Pełny tekst źródłaAversano, Lerina, Mario Luca Bernardi, Marta Cimitile i Riccardo Pecori. "Fuzzy Neural Networks to Detect Parkinson Disease". W 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177948.
Pełny tekst źródłaTaur, J. S., i S. Y. Kung. "Fuzzy-decision neural networks". W Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319184.
Pełny tekst źródłaWang, Jing, Chi-Hsu Wang i C. L. Philip Chen. "Finding the capacity of Fuzzy Neural Networks (FNNs) via its equivalent fully connected neural networks (FFNNs)". W 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007473.
Pełny tekst źródłaEl-Shafei, A., T. A. F. Hassan, A. K. Soliman, Y. Zeyada i N. Rieger. "Neural Network and Fuzzy Logic Diagnostics of 1X Faults in Rotating Machinery". W ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68885.
Pełny tekst źródłaIsrael, Cruz Vega, Wen Yu i Juan Jose Cordova. "Multiple fuzzy neural networks modeling with sparse data". W 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584804.
Pełny tekst źródłaRaporty organizacyjne na temat "Fuzzy neural networks"
Maurer, W. J., i F. U. Dowla. Seismic event interpretation using fuzzy logic and neural networks. Office of Scientific and Technical Information (OSTI), styczeń 1994. http://dx.doi.org/10.2172/10139515.
Pełny tekst źródłaKarakowski, Joseph A., i Hai H. Phu. A Fuzzy Hypercube Artificial Neural Network Classifier. Fort Belvoir, VA: Defense Technical Information Center, październik 1998. http://dx.doi.org/10.21236/ada354805.
Pełny tekst źródłaHuang, Z., J. Shimeld i M. Williamson. Application of computer neural network, and fuzzy set logic to petroleum geology, offshore eastern Canada. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/194121.
Pełny tekst źródłaRajagopalan, A., G. Washington, G. Rizzoni i Y. Guezennec. Development of Fuzzy Logic and Neural Network Control and Advanced Emissions Modeling for Parallel Hybrid Vehicles. Office of Scientific and Technical Information (OSTI), grudzień 2003. http://dx.doi.org/10.2172/15006009.
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