Robust convergence of Cohen-Grossberg neural networks with time-varying delays

被引：8

作者：

Xiong, Wenjun ^{[1
]}

Ma, Deyi ^{[1
]}

Liang, Jinling ^{[2
]}

机构：

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

[2] Southeast Univ, Dept Math, Nanjing 210096, Peoples R China

来源：

CHAOS SOLITONS & FRACTALS | 2009年 / 40卷 / 03期

关键词：

CONTINUOUSLY DISTRIBUTED DELAYS; STABILITY ANALYSIS; EXPONENTIAL STABILITY; HARMLESS DELAYS; DISCRETE; SYSTEMS; CRITERION;

D O I：

10.1016/j.chaos.2007.08.072

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, robust convergence is studied for the Cohen-Grossberg neural networks (CGNNs) with time-varying delays. By applying the differential inequality and the Lyapunov method, some delay-independent conditions are derived ensuring the robust CGNNs to converge, globally, uniformly and exponentially, to a ball in the state space with a pre-specified convergence rate. Finally, the effectiveness of our results are verified by an illustrative example. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：1176 / 1184

页数：9

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