Globally exponential stability of generalized Cohen-Grossberg neural networks with delays

被引：111

作者：

Hwang, CC ^{[1
]}

Cheng, CJ ^{[1
]}

Liao, TL ^{[1
]}

机构：

[1] Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan

来源：

PHYSICS LETTERS A | 2003年 / 319卷 / 1-2期

关键词：

exponential stability; neural networks; Cohen-Grossberg neural networks; Hopfield neural networks; cellular neural networks; Halanay inequality lemma;

D O I：

10.1016/j.physleta.2003.10.002

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Based on the Halanay inequality lemma, this Letter derives a new sufficient condition for the globally exponential stability of the generalized Cohen-Grossberg neural networks with delays (GDCGNNs). The GDCGNN is quite general, and can describe several well-known neural networks with and without delays, including Hopfield and cellular neural networks. It is shown that the proposed sufficient condition relies on the connection matrices and the network parameters, and that it is independent of the delay parameter. Furthermore, the presented condition is easy to check, and is less restrictive than some of the sufficient conditions proposed in previous studies. The benefits of the developed sufficient condition are demonstrated by comparing its performance in a series of examples with that of several conditions presented previously. (C) 2003 Elsevier B.V. All rights reserved.

引用

页码：157 / 166

页数：10

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