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

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3 Author(s)
Jing Wang ; Fac. of Sci. & Technol., Univ. of Macau, Macao, China ; Chen, C.L.P. ; Chi-Hsu Wang

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.

Published in:

Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on

Date of Conference:

14-17 Oct. 2012

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