Further results on exponential stability of discrete-time BAM neural networks with time-varying delays

被引：11

作者：

Shu, Yanjun ^{[1
]}

Liu, Xinge ^{[1
]}

Wang, Fengxian ^{[1
]}

Qiu, Saibing ^{[1
,2
]}

机构：

[1] Cent S Univ, Sch Math & Stat, Changsha 410083, Peoples R China

[2] Hunan City Univ, Coll Math & Comp Sci, Yiyang 413000, Peoples R China

来源：

MATHEMATICAL METHODS IN THE APPLIED SCIENCES | 2017年 / 40卷 / 11期

关键词：

discrete-time BAM neural networks; exponential stability; Lyapunov-Krasovskii functional; reciprocally convex approach; linear matrix inequalities (LMIs); GLOBAL ASYMPTOTIC STABILITY; VARIABLE DELAYS; LEAKAGE DELAYS; SYSTEMS; CRITERIA;

D O I：

10.1002/mma.4281

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper is concerned with the exponential stability for the discrete-time bidirectional associative memory neural networks with time-varying delays. Based on Lyapunov stability theory, some novel delay-dependent sufficient conditions are obtained to guarantee the globally exponential stability of the addressed neural networks. In order to obtain less conservative results, an improved Lyapunov-Krasovskii functional is constructed and the reciprocally convex approach and free-weighting matrix method are employed to give the upper bound of the difference of the Lyapunov-Krasovskii functional. Several numerical examples are provided to illustrate the effectiveness of the proposed method. Copyright (c) 2017 John Wiley & Sons, Ltd.

引用

页码：4014 / 4027

页数：14

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