An efficient genetic algorithm for TSK-type neural fuzzy identifier design

Authors:
Cheng-Jian Lin;Yong-Ji Xu;Chi-Yung Lee
Affiliations:
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Wufong Township, Taichung Conuty, Taiwan;Department of Computer Science and Information Engineering, Chaoyang University of Technology, Wufong Township, Taichung Conuty, Taiwan;Dept. of Computer Science and Information Engineering, Nankai College, Nantou County, Taiwan, R.O.C
Venue:
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Year:
2005

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Abstract

In this paper, an efficient genetic algorithm (EGA) for TSK-type neural fuzzy identifier (TNFI) is proposed for solving identification problem. For the proposed EGA method, the better chromosomes will be initially generated while the better mutation points will be determined for performing efficient mutation. The adjustable parameters of a TNFI model are coded as real number components and are searched by EGA method. The advantages of the proposed learning algorithm are that, first, it converges quickly and the obtained fuzzy rules are more precise. Secondly, the proposed EGA method only takes a few population sizes.