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Finding the near optimal learning rates 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
2012-10-01
Source PublicationProceedings 2012 International Conference on System Science and Engineering, ICSSE 2012
Pages137-142
AbstractIn this paper, Fuzzy Neural Network (FNN) is transformed into an equivalent fully connected three layer neural network, or FFNN. Based on the FFNN, BP training algorithm is derived. To improve convergent rate, a new method to find near optimal learning rates for FFNN is proposed. Illustrative examples are presented to check the validity of the proposed theory and algorithms. Simulation results show satisfactory results. Finding near optimal learning rates 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.
KeywordBack Propagations Fuzzy Logic Fuzzy Neural Networks Gradient Descent Neural Networks Optimal training
DOI10.1109/ICSSE.2012.6257164
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.. Finding the near optimal learning rates of Fuzzy Neural Networks (FNNs) via its equivalent fully connected neural networks (FFNNs)[C],2012:137-142.
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