Relevant bibliographies by topics / Fuzzy neural networks

Academic literature on the topic 'Fuzzy neural networks'

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Published: 4 June 2021
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Journal articles on the topic "Fuzzy neural networks"

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Rao, D. H. "Fuzzy Neural Networks." IETE Journal of Research 44, no. 4-5 (July 1998): 227–36. http://dx.doi.org/10.1080/03772063.1998.11416049.

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Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.

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Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.
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OH, SUNG-KWUN, DONG-WON KIM, and WITOLD PEDRYCZ. "HYBRID FUZZY POLYNOMIAL NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 03 (June 2002): 257–80. http://dx.doi.org/10.1142/s0218488502001478.

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We propose a hybrid architecture based on a combination of fuzzy systems and polynomial neural networks. The resulting Hybrid Fuzzy Polynomial Neural Networks (HFPNN) dwells on the ideas of fuzzy rule-based computing and polynomial neural networks. The structure of the network comprises of fuzzy polynomial neurons (FPNs) forming the nodes of the first (input) layer of the HFPNN and polynomial neurons (PNs) that are located in the consecutive layers of the network. In the FPN (that forms a fuzzy inference system), the generic rules assume the form "if A then y = P(x) " where A is fuzzy relation in the condition space while P(x) is a polynomial standing in the conclusion part of the rule. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as constant, linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are considered. Each PN of the network realizes a polynomial type of partial description (PD) of the mapping between input and out variables. HFPNN is a flexible neural architecture whose structure is based on the Group Method of Data Handling (GMDH) and developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. The experimental part of the study involves two representative numerical examples such as chaotic time series and Box-Jenkins gas furnace data.
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ISHIBUCHI, Hisao, Hidehiko OKADA, and Hideo TANAKA. "Fuzzy Neural Networks with Fuzzy Weights." Transactions of the Institute of Systems, Control and Information Engineers 6, no. 3 (1993): 137–48. http://dx.doi.org/10.5687/iscie.6.137.

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Geng, Z. Jason. "Fuzzy CMAC Neural Networks." Journal of Intelligent and Fuzzy Systems 3, no. 1 (1995): 87–102. http://dx.doi.org/10.3233/ifs-1995-3108.

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Dunyak, James, and Donald Wunsch. "Fuzzy number neural networks." Fuzzy Sets and Systems 108, no. 1 (November 1999): 49–58. http://dx.doi.org/10.1016/s0165-0114(97)00339-4.

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Virgil Negoita, Constantin. "Neural Networks as Fuzzy Systems." Kybernetes 23, no. 3 (April 1, 1994): 7–9. http://dx.doi.org/10.1108/03684929410059000.

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Any fuzzy system is a knowledge‐based system which implies an inference engine. Proposes neural networks as a means of performing the inference. Using the Theorem of Representation proposes an encoding scheme that allows the neural network to be trained to perform modus ponens.
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Reddy, Bapatu Siva Kumar, and P. Vishnu Vardhan. "Novel Alphabet Deduction Using MATLAB by Neural Networks and Comparison with the Fuzzy Classifier." Alinteri Journal of Agriculture Sciences 36, no. 1 (June 29, 2021): 623–28. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21088.

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Aim: The study aims to identify or recognize the alphabets using neural networks and fuzzy classifier/logic. Methods and materials: Neural network and fuzzy classifier are used for comparing the recognition of characters. For each classifier sample size is 20. Character recognition was developed using MATLAB R2018a, a software tool. The algorithm is again compared with the Fuzzy classifier to know the accuracy level. Results: Performance of both fuzzy classifier and neural networks are calculated by the accuracy value. The mean value of the fuzzy classifier is 82 and the neural network is 77. The recognition rate (accuracy) with the data features is found to be 98.06%. Fuzzy classifier shows higher significant value of P=0.002 < P=0.005 than the neural networks in recognition of characters. Conclusion: The independent tests for this study shows a higher accuracy level of alphabetical character recognition for Fuzzy classifier when compared with neural networks. Henceforth, the fuzzy classifier shows higher significant than the neural networks in recognition of characters.
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Purushothaman, G., and N. B. Karayiannis. "Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks." IEEE Transactions on Neural Networks 8, no. 3 (May 1997): 679–93. http://dx.doi.org/10.1109/72.572106.

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Blake, J. "The implementation of fuzzy systems, neural networks and fuzzy neural networks using FPGAs." Information Sciences 112, no. 1-4 (December 1998): 151–68. http://dx.doi.org/10.1016/s0020-0255(98)10029-4.

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Dissertations / Theses on the topic "Fuzzy neural networks"

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Glackin, Cornelius. "Fuzzy spiking neural networks." Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.505831.

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Brande, Julia K. Jr. "Computer Network Routing with a Fuzzy Neural Network." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29685.

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The growing usage of computer networks is requiring improvements in network technologies and management techniques so users will receive high quality service. As more individuals transmit data through a computer network, the quality of service received by the users begins to degrade. A major aspect of computer networks that is vital to quality of service is data routing. A more effective method for routing data through a computer network can assist with the new problems being encountered with today's growing networks. Effective routing algorithms use various techniques to determine the most appropriate route for transmitting data. Determining the best route through a wide area network (WAN), requires the routing algorithm to obtain information concerning all of the nodes, links, and devices present on the network. The most relevant routing information involves various measures that are often obtained in an imprecise or inaccurate manner, thus suggesting that fuzzy reasoning is a natural method to employ in an improved routing scheme. The neural network is deemed as a suitable accompaniment because it maintains the ability to learn in dynamic situations. Once the neural network is initially designed, any alterations in the computer routing environment can easily be learned by this adaptive artificial intelligence method. The capability to learn and adapt is essential in today's rapidly growing and changing computer networks. These techniques, fuzzy reasoning and neural networks, when combined together provide a very effective routing algorithm for computer networks. Computer simulation is employed to prove the new fuzzy routing algorithm outperforms the Shortest Path First (SPF) algorithm in most computer network situations. The benefits increase as the computer network migrates from a stable network to a more variable one. The advantages of applying this fuzzy routing algorithm are apparent when considering the dynamic nature of modern computer networks.
Ph. D.
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Pirovolou, Dimitrios K. "The tracking problem using fuzzy neural networks." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14824.

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Frayman, Yakov, and 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.

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This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.
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Leng, 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.

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RENTERIA, 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.

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FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO
Este 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.
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Kim, Hung-man. "Implementing adaptive fuzzy logic controllers with neural networks." Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187160.

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The goal of intelligent control is to achieve control objectives for complex systems where it is impossible or infeasible to develop a mathematical system model but expert skills and heuristic knowledge from human experiences are available for control purposes. To this end, an intelligent control system must have the essential characteristics of human control experiences, i.e., linguistic knowledge representation, which facilitates the process of knowledge acquisition and transfer, and adaptive knowledge evolution or learning, which leads to the improvement in system performance and knowledge. This dissertation presents an efficient approach that combines fuzzy logic and neural networks to capture these two important features required for an intelligent control system. A design method for adaptive neuro-fuzzy controllers has been proposed using structured neuro-fuzzy networks. The structured neuro-fuzzy networks consist of three types of subnets for pattern recognition, fuzzy reasoning, and control synthesis, respectively. Each subnet is constructed directly from the decision-making procedure of fuzzy logic based control systems. In this way, a one-to-one mapping between a fuzzy logic based control system and a structured neuro-fuzzy network is established. This mapping enables us to create a knowledge structure within neural networks based on fuzzy logic, and to give a learning ability to fuzzy controls using neural networks. From the perspective of neural networks, the proposed design method offers a mechanism to: construct networks with heuristic knowledge, instead of using digital training pairs, which are much more difficult to get, build decision structures into networks, which divide a network into several functional regions and make the network no longer just as a black-box function approximator, and conduct network learning in a distributed fashion, i.e., each sub-network of different functional regions can learn its own function independently. On the other hand, from the perspective of fuzzy logic, the proposed design method provides a tool to: refine membership functions, inference procedures, and defuzzification algorithms of fuzzy control systems; generate new fuzzy control rules so that fuzzy control systems can adapt to gradual changes in environments and implement parallel execution of rule matching, firing, and defuzzification. Several simulation studies have been conducted to demonstrate the use of the structured neuro-fuzzy networks. The effectiveness of the proposed design method has been clearly shown by the results of these studies. These results have also indicated that fuzzy logic and neural networks are complementary and their combination is ideal to achieve the goal of intelligent control.
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Gabrys, 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.

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Bordignon, Fernando Luis. "Aprendizado extremo para redes neurais fuzzy baseadas em uninormas." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259061.

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Orientador: Fernando Antônio Campos Gomide
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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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
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Aimejalii, K., Keshav P. Dahal, and M. Alamgir Hossain. "GA-based learning algorithms to identify fuzzy rules for fuzzy neural networks." IEEE, 2007. http://hdl.handle.net/10454/2553.

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Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). However, there is no systematic design procedure at present. In this paper we present a genetic algorithm (GA) based learning algorithm to make use of the known membership function to identify the fuzzy rules form a large set of all possible rules. The proposed learning algorithm initially considers all possible rules then uses the training data and the fitness function to perform ruleselection. The proposed GA based learning algorithm has been tested with two different sets of training data. The results obtained from the experiments are promising and demonstrate that the proposed GA based learning algorithm can provide a reliable mechanism for fuzzy rule selection.
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Books on the topic "Fuzzy neural networks"

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Abe, Shigeo. Neural Networks and Fuzzy Systems. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6253-5.

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Rao, Valluru. C++ neural networks and fuzzy logic. 2nd ed. New York: MIS:Press, 1995.

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Rao, Valluru. C++ neural networks and fuzzy logic. New York: MIS:Press, 1993.

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Yager, R. R. Fuzzy sets, neural networks and soft computing. New York: Van Nostrand Reinhold, 1994.

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International conference (February 12-14, 1996 Lausanne, Switzerland). Microeletronics for neural networks and fuzzy systems. Los Alamitos, Calif: IEEE, 1996.

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Furuhashi, Takeshi, and Yoshiki Uchikawa, eds. Fuzzy Logic, Neural Networks, and Evolutionary Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7.

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1941-, Yager Ronald R., and Zadeh Lotfi Asker, eds. Fuzzy sets, neural networks, and soft computing. New York: Van Nostrand Reinhold, 1994.

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Fuzzy sets engineering. Boca Raton, Fla: CRC Press, 1995.

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Rudolf, Kruse, and Klawonn F, eds. Foundations of neuro-fuzzy systems. Chichester: John Wiley, 1997.

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Stavroulakis, Peter. Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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Book chapters on the topic "Fuzzy neural networks"

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Rojas, Raúl. "Fuzzy Logic." In Neural Networks, 287–308. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4_11.

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Czogała, Ernest, and Jacek Łęski. "Artificial neural networks." In 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.

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Singh, Himanshu, and Yunis Ahmad Lone. "Fuzzy Neural Networks." In Deep Neuro-Fuzzy Systems with Python, 199–221. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5361-8_6.

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Fullér, Robert. "Fuzzy neural networks." In Introduction to Neuro-Fuzzy Systems, 171–254. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1852-9_3.

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Prasad, Nadipuram Ram R. "Neural Networks and Fuzzy Logic." In Fuzzy Systems, 381–401. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5505-6_11.

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Abe, Shigeo. "Other Neural Networks." In Neural Networks and Fuzzy Systems, 93–125. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6253-5_4.

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Fullér, Robert. "Artificial neural networks." In Introduction to Neuro-Fuzzy Systems, 133–70. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1852-9_2.

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Singh, Himanshu, and Yunis Ahmad Lone. "Artificial Neural Networks." In Deep Neuro-Fuzzy Systems with Python, 157–98. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5361-8_5.

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Jin, Yaochu. "Artificial Neural Networks." In 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.

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Tsoukalas, L. H., A. Ikonomopoulos, and R. E. Uhrig. "Fuzzy neural control." In Artificial Neural Networks for Intelligent Manufacturing, 413–34. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0713-6_15.

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Conference papers on the topic "Fuzzy neural networks"

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Kumar, Manish, and Devendra P. Garg. "Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59589.

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Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have been applied to control an inverted pendulum and results have been compared for two different fuzzy logic controllers developed with the help of neural networks. The first neural network emulates a PD controller, while the second controller is developed based on MPC. The proposed approach can be applied to learn fuzzy logic controller parameter online via the use of dynamic backpropagation. The results show that the Neuro-Fuzzy approaches were able to learn rule base and identify membership function parameters accurately.
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Aouiti, Chaouki, Farah Dridi, and Fakhri Karray. "New Results on Neutral Type Fuzzy Based Cellular Neural Networks." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491607.

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Ji-Cheng Duan and Fu-Lai Chung. "Cascading fuzzy neural networks." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.793206.

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Amina, Mahdi, and Vassilis S. Kodogiannis. "Load forecasting using fuzzy wavelet neural networks." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007492.

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Kowalski, Piotr A., and Tomasz Sloczynski. "Saturation in Fuzzy Flip-Flop Neural Networks." In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882672.

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Aversano, Lerina, Mario Luca Bernardi, Marta Cimitile, and Riccardo Pecori. "Fuzzy Neural Networks to Detect Parkinson Disease." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177948.

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Taur, J. S., and S. Y. Kung. "Fuzzy-decision neural networks." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319184.

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Wang, Jing, Chi-Hsu Wang, and C. L. Philip Chen. "Finding the capacity of Fuzzy Neural Networks (FNNs) via its equivalent fully connected neural networks (FFNNs)." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007473.

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El-Shafei, A., T. A. F. Hassan, A. K. Soliman, Y. Zeyada, and N. Rieger. "Neural Network and Fuzzy Logic Diagnostics of 1X Faults in Rotating Machinery." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68885.

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Abstract:
In this paper, the application of Neural Networks and Fuzzy Logic to the diagnosis of Faults in Rotating Machinery is investigated. The Learning-Vector-Quantization (LVQ) Neural Network is applied in series and in parallel to a Fuzzy inference engine, to diagnose 1x faults. The faults investigated are unbalance, misalignment, and structural looseness. The method is applied to a test rig [1], and the effectiveness of the integrated Neural Network and Fuzzy Logic method is illustrated.
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Israel, Cruz Vega, Wen Yu, and Juan Jose Cordova. "Multiple fuzzy neural networks modeling with sparse data." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584804.

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Reports on the topic "Fuzzy neural networks"

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Maurer, W. J., and F. U. Dowla. Seismic event interpretation using fuzzy logic and neural networks. Office of Scientific and Technical Information (OSTI), January 1994. http://dx.doi.org/10.2172/10139515.

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Karakowski, Joseph A., and Hai H. Phu. A Fuzzy Hypercube Artificial Neural Network Classifier. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada354805.

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Huang, Z., J. Shimeld, and 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.

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Rajagopalan, A., G. Washington, G. Rizzoni, and 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), December 2003. http://dx.doi.org/10.2172/15006009.

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