Eliciting transparent fuzzy model using differential evolution

被引：38
作者：
Eftekhari, M. [1 ]
Katebi, S. D. [1 ]
Karimi, M. [1 ]
Jahanmiri, A. H. [1 ]
机构：
[1] Shiraz Univ, Sch Engn, Shiraz, Iran
来源：
APPLIED SOFT COMPUTING | 2008年 / 8卷 / 01期
关键词：
differential evolution; eliciting; fuzzy; model; clustering; mackey-glass; evaporator; CSTR;
D O I：
10.1016/j.asoc.2007.02.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper a new technique for eliciting a fuzzy inference system (FIS) from data for nonlinear systems is proposed. The strategy is conducted in two phases: in the first one, subtractive clustering is applied in order to extract a set of fuzzy rules, in the second phase, the generated fuzzy rule base is refined and redundant rules are removed on the basis of an interpretability measure. Finally the centers and widths of the Membership Functions (MFs) are tuned by means differential evolution. Case studies are presented to illustrate the efficiency and accuracy of the proposed approach. The results obtained are compared and contrasted with those obtained from a conventionally neuro-fuzzy technique and the superiority of the proposed approach is highlighted. (C) 2007 Elsevier B. V. All rights reserved.
引用
收藏
页码：466 / 476
页数：11
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