Residential College | false |
Status | 已發表Published |
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 | |
Conference Name | 2012 International Conference on System Science and Engineering, ICSSE 2012 |
Source Publication | Proceedings 2012 International Conference on System Science and Engineering, ICSSE 2012 |
Pages | 137-142 |
Conference Date | 30 June 2012through 2 July 2012 |
Conference Place | Dalian, Liaoning, China |
Abstract | In 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. |
Keyword | Back Propagations Fuzzy Logic Fuzzy Neural Networks Gradient Descent Neural Networks Optimal Training |
DOI | 10.1109/ICSSE.2012.6257164 |
URL | View the original |
Language | 英語English |
Scopus ID | 2-s2.0-84866658670 |
Fulltext Access | |
Citation statistics | |
Document Type | Conference paper |
Collection | DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Affiliation | 1.Universidade de Macau 2.National Chiao Tung University Taiwan |
First Author Affilication | University of Macau |
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. |
APA | Wang J.., Chen C.L.P.., & Wang C.-H. (2012). Finding the near optimal learning rates of Fuzzy Neural Networks (FNNs) via its equivalent fully connected neural networks (FFNNs). Proceedings 2012 International Conference on System Science and Engineering, ICSSE 2012, 137-142. |
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