Global exponential stability for discrete-time neural networks with variable delays

被引：72

作者：

Chen, Wu-Hua ^{[1
]}

Lu, Xiaomei ^{[1
]}

Liang, Dong-Ying ^{[1
]}

机构：

[1] Guangxi Univ, Coll Math & Informat Sci, Nanning 530004, Guangxi, Peoples R China

来源：

PHYSICS LETTERS A | 2006年 / 358卷 / 03期

基金：

中国国家自然科学基金;

关键词：

neural networks; discrete-time systems; variable delays; globally exponential stability; delay-dependent criteria; M-matrix;

D O I：

10.1016/j.physleta.2006.05.014

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

This Letter provides new exponential stability criteria for discrete-time neural networks with variable delays. The main technique is to reduce exponential convergence estimation of the neural network solution to that of one component of the corresponding solution by constructing Lyapunov function based on M-matrix. By introducing the tuning parameter diagonal matrix, the delay-independent and delay-dependent exponential stability conditions have been unified in the same mathematical formula. The effectiveness of the new results are illustrated by three examples. (c) 2006 Elsevier B.V. All rights reserved.

引用

页码：186 / 198

页数：13

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