Exponential stability of delayed Hopfield neural networks with various activation functions and polytopic uncertainties

被引：10

作者：

Phat, Vu N. ^{[1
]}

Nam, Phan T. ^{[2
]}

机构：

[1] VAST, Inst Math, Hanoi 10307, Vietnam

[2] Qui Nhon Univ, Dept Math, Binh Dinh, Vietnam

来源：

PHYSICS LETTERS A | 2010年 / 374卷 / 25期

关键词：

Hopfield neural networks; Exponential stability; Polytopic systems; Time-varying delays; Lyapunov function; Linear matrix inequalities; GLOBAL ASYMPTOTIC STABILITY; TIME-VARYING DELAYS; ROBUST STABILITY; VARIABLE DELAYS; SYSTEMS;

D O I：

10.1016/j.physleta.2010.04.018

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

This Letter deals with the problem of exponential stability for a class of delayed Hopfield neural networks. Based on augmented parameter-dependent Lyapunov-Krasovskii functionals, new delay-dependent conditions for the global exponential stability are obtained for two cases of time-varying delays: the delays are differentiable and have an upper bound of the delay-derivatives, and the delays are bounded but not necessary to be differentiable. The conditions are presented in terms of linear matrix inequalities, which allow to compute simultaneously two bounds that characterize the exponential stability rate of the solution. Numerical examples are included to illustrate the effectiveness of our results. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：2527 / 2533

页数：7

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