A compensation-based recurrent fuzzy neural network for dynamic system identification

被引：24

作者：

Lin, CJ ^{[1
]}

Chen, CH ^{[1
]}

机构：

[1] Chaoyang Univ Technol, Dept Comp Sci & Informat Engn, Wufong Township 41349, Taichung County, Taiwan

来源：

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 2006年 / 172卷 / 02期

关键词：

identification; chaotic system; fuzzy neural networks; compensatory operator; recurrent networks;

D O I：

10.1016/j.ejor.2004.11.007

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

In this paper, a type of compensation-based recurrent fuzzy neural network (CRFNN) for identifying dynamic systems is proposed. The proposed CRFNN uses a compensation-based fuzzy reasoning method, and has feedback connections added in the rule layer of the CRFNN. The compensation-based fuzzy reasoning method can make the fuzzy logic system more adaptive and effective, and the additional feedback connections can solve temporal problems. The CRFNN model is proven to be a universal approximator in this paper. Moreover, an online learning algorithm is proposed to automatically construct the CRFNN. The results from simulations of identifying dynamic systems have shown that the convergence speed of the proposed method is faster than the convergence speed of conventional methods and that only a small number of tuning parameters are required. (c) 2004 Elsevier B.V. All rights reserved.

引用

页码：696 / 715

页数：20

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