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On the conjugate gradients (CG) training algorithm of fuzzy neural networks (FNNs) via its equivalent fully connected neural networks (FFNNs)
Wang J.1; Chen C.L.P.1; Wang C.-H.2
2012-12-01
Source PublicationConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Pages2446-2451
AbstractIn this paper, Fuzzy Neural Network (FNN) is transformed into an equivalent fully connected three layer neural network, or FFNN. Based on the FFNN, conjugate gradients (CG) training algorithm is derived to tune both the premise and consequent part of FNN, and apparently increase the speed of convergence. Illustrative examples are presented to check the validity of the proposed theory and algorithms. Simulation achieves satisfactory results. Developing CG training algorithm for FNN via its equivalent FFNN has its emerging values in all engineering applications using FNN, such as intelligent adaptive control, pattern recognition, and signal processing..., etc. © 2012 IEEE.
Keywordconjugate gradients Fuzzy Logic Fuzzy Neural Networks Gradient Descent Neural Networks
DOI10.1109/ICSMC.2012.6378110
URLView the original
Language英語
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Universidade de Macau
2.National Chiao Tung University Taiwan
Recommended Citation
GB/T 7714
Wang J.,Chen C.L.P.,Wang C.-H.. On the conjugate gradients (CG) training algorithm of fuzzy neural networks (FNNs) via its equivalent fully connected neural networks (FFNNs)[C],2012:2446-2451.
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