An asymmetry-similarity-measure-based neural fuzzy inference system

Authors:
Cheng-Jian Lin;Wen-Hao Ho
Affiliations:
Department of Computer Science and Information Engineering, Chaoyang University of Technology, No. 168, Jifong E. Rd., Wufong Township, Taichung County 41349, Taiwan;Department of Computer Science and Information Engineering, Chaoyang University of Technology, No. 168, Jifong E. Rd., Wufong Township, Taichung County 41349, Taiwan
Venue:
Fuzzy Sets and Systems
Year:
2005

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Abstract

In this paper, a new asymmetry-similarity-measure-based neural fuzzy inference system (ASM-NFIS) is proposed. A pseudo-Gaussian membership function can provide a neural fuzzy inference system which has a higher flexibility and can approach the optimized result more accurately. An on-line self-constructing learning algorithm is proposed to automatically construct the ASM-NFIS. It consists of structure learning and parameter learning that would create adaptive fuzzy logic rules. The structure learning is based on the similarity measure of asymmetric Gaussian membership functions, and the parameter learning is based on a supervised gradient descent method. Computer simulations were conducted to illustrate the performance and applicability of the proposed model.