Relevant bibliographies by topics / Fuzzy neural networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fuzzy neural networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Fuzzy neural networks"

1

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

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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 (1999): 49–58. http://dx.doi.org/10.1016/s0165-0114(97)00339-4.

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Md., Musa Khan. "Comparison of Selection Method of a Membership Function for Fuzzy Neural Networks." International Journal of Case Studies 6, no. 11 (2017): 71–77. https://doi.org/10.5281/zenodo.3538605.

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Fuzzy neural networks are learning machine that realize the parameters of a fuzzy system (i.e., fuzzy sets, fuzzy rules) by exploiting approximation techniques from neural networks. In this paper, we tend to illustrate a general methodology, based on statistical analysis of the training data, for the choice of fuzzy membership functions to be utilized in reference to fuzzy neural networks. Fuzzy neural networks give for the extraction of fuzzy rules for from artificial neural network architectures. First, the technique is represented and so illustrated utilizing two experimental examinations f
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Yang, Xin. "Quantum fuzzy neural network based on fuzzy number." Frontiers in Computing and Intelligent Systems 3, no. 2 (2023): 99–105. http://dx.doi.org/10.54097/fcis.v3i2.7524.

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Neural network is one of the AI algorithms commonly used to process data, and has an extremely important position in scenarios such as image recognition, classification, and machine translation. With the increase of data volume explosion, the required computing power of neural networks is also significantly increased. The emergence of quantum neural networks improves the computational power of neural networks, but the accuracy of neural networks and quantum neural networks is not high in the face of the complexity and uncertainty of big data. In order to improve the efficiency and accuracy, th
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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 (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 (1998): 151–68. http://dx.doi.org/10.1016/s0020-0255(98)10029-4.

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Dunyak, James P., and Donald Wunsch. "Fuzzy regression by fuzzy number neural networks." Fuzzy Sets and Systems 112, no. 3 (2000): 371–80. http://dx.doi.org/10.1016/s0165-0114(97)00393-x.

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Mosleh, M., M. Otadi, and S. Abbasbandy. "Fuzzy polynomial regression with fuzzy neural networks." Applied Mathematical Modelling 35, no. 11 (2011): 5400–5412. http://dx.doi.org/10.1016/j.apm.2011.04.039.

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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
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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
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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<br>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 descr
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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.
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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<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>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<br>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 con
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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
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Books on the topic "Fuzzy neural networks"

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Abe, Shigeo. Neural Networks and Fuzzy Systems. 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. MIS:Press, 1993.

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

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

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

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

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

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

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Horst, Bunke, and Kandel Abraham, eds. Neuro-fuzzy pattern recognition. World Scientific, 2000.

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Aliev, Rafik Aziz, and Babek Ghalib Guirimov. Type-2 Fuzzy Neural Networks and Their Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09072-6.

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Book chapters on the topic "Fuzzy neural networks"

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Rojas, Raúl. "Fuzzy Logic." In Neural Networks. 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. 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. 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. 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. 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. 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. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1852-9_2.

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Stavroulakis, Peter. "Appendix A. Overview of Neural Networks." In Neuro-Fuzzy and Fuzzy-Neural Applications in Telecommunications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18762-9_8.

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Singh, Himanshu, and Yunis Ahmad Lone. "Artificial Neural Networks." In Deep Neuro-Fuzzy Systems with Python. 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. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1771-3_3.

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Conference papers on the topic "Fuzzy neural networks"

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Petrova, A. K., and S. E. Abramkin. "Hybrid Fuzzy Neural Networks (HFNNs): A Study on Combining Fuzzy Logic and Neural Networks." In 2025 XXVIII International Conference on Soft Computing and Measurements (SCM). IEEE, 2025. https://doi.org/10.1109/scm66446.2025.11060037.

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Kowalski, Piotr A., and Tomasz Sloczynski. "Mitigating Saturation in Fuzzy-Flip-Flop Neural Networks Trained with Memetic PSO Algorithm." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10611809.

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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
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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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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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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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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), 1994. http://dx.doi.org/10.2172/10139515.

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Willson. L51756 State of the Art Intelligent Control for Large Engines. Pipeline Research Council International, Inc. (PRCI), 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other task
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Karakowski, Joseph A., and Hai H. Phu. A Fuzzy Hypercube Artificial Neural Network Classifier. Defense Technical Information Center, 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), 2003. http://dx.doi.org/10.2172/15006009.

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