On the conjugate gradients (CG) training algorithm of fuzzy neural networks (FNNs) via its equivalent fully connected neural networks (FFNNs)

Jing Wang*, C. L.Philip Chen, Chi-Hsu Wang

*Corresponding author for this work

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

In 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.

原文English
主出版物標題Proceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
頁面2446-2451
頁數6
DOIs
出版狀態Published - 1 十二月 2012
事件2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
持續時間: 14 十月 201217 十月 2012

出版系列

名字Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN(列印)1062-922X

Conference

Conference2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
國家Korea, Republic of
城市Seoul
期間14/10/1217/10/12

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