Fuzzy system identification using an adaptive learning rule with terminal attractors

被引：18

作者：

Lin, CK ^{[1
]}

Wang, SD ^{[1
]}

机构：

[1] Natl Taiwan Univ, Dept Elect Engn, Taipei 106, Taiwan

来源：

FUZZY SETS AND SYSTEMS | 1999年 / 101卷 / 03期

关键词：

fuzzy model; terminal attractor; adaptive learning rule; system identification;

D O I：

10.1016/S0165-0114(97)00106-1

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

A fuzzy model with an adaptive learning rule is proposed to approximate a class of nonlinear systems. The proposed adaptive learning rule uses the concept of terminal attractors and guarantees that the identification is stable and fast convergence in finite time. Another subject of this paper is to determine the appropriate rule number of the fuzzy model required for a desired modeling error. A learning algorithm is also introduced to identify the centers of membership functions of fuzzy outputs. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：343 / 352

页数：10

共 18 条

[1] HYBRID NEURAL NETS CAN BE FUZZY CONTROLLERS AND FUZZY EXPERT-SYSTEMS [J].