Structure identification and parameter optimization for non-linear fuzzy modeling

被引：60
作者：
Evsukoff, A
Branco, ACS
Galichet, S
机构：
[1] Inst Doris Ferraz Aragon, ILTC, BR-24030080 Niteroi, RJ, Brazil
[2] CESALP, LAMII, F-74016 Annecy, France
来源：
FUZZY SETS AND SYSTEMS | 2002年 / 132卷 / 02期
关键词：
fuzzy systems; non-linear system identification; non-linear modeling; least-squares optimization; approximation;
D O I：
10.1016/S0165-0114(02)00111-2
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This work presents a method for non-linear fuzzy model identification. The main characteristic of the method is the automatic determination of the number and position of the fuzzy sets in the domain of each variable. The resultant fuzzy rule base allows model interpretation by domain experts. The main contribution of this work is a formulation that allows the optimization of output parameters by a least-squares error (LSE) minimization. A numerical solution of the LSE problem is developed based on the singular value decomposition of the regressor matrix. The whole methodology is applied to some numerical examples found in the literature. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：173 / 188
页数：16
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