A modified gradient-based neuro-fuzzy learning algorithm and its convergence

被引：72

作者：

Wu, Wei ^{[1
]}

Li, Long ^{[1
]}

Yang, Jie ^{[1
]}

Liu, Yan ^{[2
]}

机构：

[1] Dalian Univ Technol, Dept Appl Math, Dalian 116024, Peoples R China

[2] Dalian Polytech Univ, Dept Appl Math, Dalian 116034, Peoples R China

来源：

INFORMATION SCIENCES | 2010年 / 180卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Zero-order Takagi-Sugeno inference system; Modified gradient-based neuro-fuzzy learning algorithm; Convergence; Constant learning rate; Gaussian membership function; INFERENCE SYSTEM; IDENTIFICATION; NETWORK;

D O I：

10.1016/j.ins.2009.12.030

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Neuro-fuzzy approach is known to provide an adaptive method to generate or tune fuzzy rules for fuzzy systems. In this paper, a modified gradient-based neuro-fuzzy learning algorithm is proposed for zero-order Takagi-Sugeno inference systems. This modified algorithm, compared with conventional gradient-based neuro-fuzzy learning algorithm, reduces the cost of calculating the gradient of the error function and improves the learning efficiency. Some weak and strong convergence results for this algorithm are proved, indicating that the gradient of the error function goes to zero and the fuzzy parameter sequence goes to a fixed value, respectively. A constant learning rate is used. Some conditions for the constant learning rate to guarantee the convergence are specified. Numerical examples are provided to support the theoretical findings. (C) 2010 Elsevier Inc. All rights reserved.

引用

页码：1630 / 1642

页数：13

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