The design of beta basis function neural network and beta fuzzy systems by a hierarchical genetic algorithm

被引：28
|
作者：
Aouiti, C
Alimi, AM
Karray, F
Maalej, A
机构：
[1] Univ Waterloo, Dept Elect & Comp Engn, PAMI, Pattern Anal & Machine Intelligence Lab, Waterloo, ON N2L 3G1, Canada
[2] Univ 7 Novembre Carthage, Fac Sci Bizerta, Bizerte, Tunisia
[3] Univ Sfax, ENIS, Dept Elect Engn, REGIM,Res Grp Intelligent Machines, Sfax, Tunisia
[4] Univ Sfax, Dept Mech Engn, LASEM, Lab Electromech Syst,ENIS, Sfax, Tunisia
来源：
FUZZY SETS AND SYSTEMS | 2005年 / 154卷 / 02期
关键词：
genetic algorithms; beta function; neural networks; functions approximation; learning; beta fuzzy systems; beta bases function neural networks;
D O I：
10.1016/j.fss.2005.01.013
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
We propose an evolutionary method for the design of beta basis function neural networks (BBFNN) and of beta fuzzy systems (BFS). Classical training algorithms start with a predetermined network structure for neural networks and with a predetermined number of fuzzy rules for fuzzy systems. Generally speaking both the neural network and the fuzzy systems are either insufficient or overcomplicated. This paper describes a hierarchical genetic learning model of the BBFNN and the BFS. In order to examine the performance of the proposed algorithm, it is used for functional approximation problem for the case of BBFNN and for the identification of an induction machine fuzzy plant model for the case of BFS. The results obtained have been encouraging. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码：251 / 274
页数：24
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