A new training algorithm for HHFNN based on Gaussian membership function for approximation

Authors:
Shuang Feng;Hongxing Li;Dan Hu
Affiliations:
School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China;Institute of Fuzzy Information Processing and Machine Intelligence, Institute of Automation, School of Electronics and Information Engineering, Dalian University of Technology, Dalian 116024, Chin ...;College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
Venue:
Neurocomputing
Year:
2009

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Abstract

A new training algorithm for hierarchical hybrid fuzzy-neural networks (HHFNN) based on Gaussian membership function is proposed in this paper. This new algorithm adjusts the widths of Gaussian membership functions of the IF parts of fuzzy rules in the lower fuzzy sub-systems, and updates the weights and bias terms of the upper neural network by gradient-descent method. Two advantages of the proposed algorithm are shown in this paper: firstly, its parameters are usually fewer, compared with the existing training algorithm for HHFNN and standard BP algorithm; secondly, it outperforms the latter two algorithms in accuracy according to simulation results.