Efficient search for fuzzy models using genetic algorithm

被引：13
|
作者：
Matsushita, S
Furuhashi, T
Tsutsui, H
Uchikawa, Y
机构：
[1] Nagoya Municipal Ind Res Inst, Atsuta Ku, Nagoya, Aichi 456, Japan
[2] Nagoya Univ, Dept Informat Elect, Chikusa Ku, Nagoya, Aichi 46401, Japan
[3] Yamatake Honeywell Co Ltd, Hodogaya Ku, Yokohama, Kanagawa 240, Japan
来源：
INFORMATION SCIENCES | 1998年 / 110卷 / 1-2期
关键词：
fuzzy modeling; genetic algorithm; fuzzy neural networks;
D O I：
10.1016/S0020-0255(97)10084-6
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Fuzzy modeling is one of the promising methods for describing nonlinear systems. Determination of antecedent structure of fuzzy model, i.e. input variables and number of membership functions for the inputs, has been one of the most important problems of the fuzzy modeling, The authors have proposed a hierarchical fuzzy modeling method using Fuzzy Neural Networks (FNN) and Genetic Algorithm (GA). This method can identify fuzzy models of nonlinear objects with strong nonlinearities. The disadvantage of this method is that the training of FNN is time consuming. This paper presents a quick method for rough search for proper structures in the antecedent of fuzzy models. The fine tuning of the acquired rough model is done by the FNNs. This modeling method is quite efficient to identify precise fuzzy models of systems with strong nonlinearities. A simulation is done to show the effectiveness of the proposed method. (C) 1998 Published by Elsevier Science Inc. All rights reserved.
引用
收藏
页码：41 / 50
页数：10
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