Exponential convergence estimates for neural networks with discrete and distributed delays

被引：21

作者：

Fang, Shengle ^{[1
]}

Jiang, Minghui ^{[1
]}

Wang, Xiaohong ^{[1
]}

机构：

[1] China Three Gorges Univ, Inst Nonlinear Complex Syst, Yichang 443002, Hubei, Peoples R China

来源：

NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS | 2009年 / 10卷 / 02期

关键词：

Neural networks; Exponential stability; Linear matrix inequality; Distributed delays; Lyapunov-Krasovskii functional; TIME-VARYING DELAYS; GLOBAL ASYMPTOTIC STABILITY; LMI APPROACH; CRITERIA; SYSTEMS;

D O I：

10.1016/j.nonrwa.2007.10.024

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, the problems of determining the global exponential stability and estimating the exponential convergence rate are investigated for a class of neural networks with mixed discrete and distributed time-varying delays. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is exploited to establish sufficient easy-to-test conditions for the neural networks to be globally exponentially stable, which can be readily solved by using the numerically efficient Matlab LMI toolbox. Three numerical examples are provided to demonstrate the effectiveness of the proposed results. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：702 / 714

页数：13

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