Existence and global asymptotic stability of positive periodic solution of delayed Cohen-Grossberg neural networks

被引：21

作者：

Hien, L. V. ^{[1
]}

Loan, T. T. ^{[1
]}

Trang, B. T. Huyen ^{[1
]}

Trinh, H. ^{[2
]}

机构：

[1] Hanoi Natl Univ Educ, Dept Math, Hanoi, Vietnam

[2] Deakin Univ, Sch Engn, Geelong, Vic 3217, Australia

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 240卷

关键词：

Cohen-Grossberg neural networks; Non-autonomous systems; Periodic solutions; Time-varying delays; M-matrix; EXPONENTIAL STABILITY; DISCRETE; CRITERIA; DESIGN; DYNAMICS; LEAKAGE;

D O I：

10.1016/j.amc.2014.04.078

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, a class of periodic Cohen-Grossberg neural networks with discrete and distributed time-varying delays is considered. By an extension of the Lyapunov-Krasovskii functional method, a novel criterion for the existence and uniqueness and global asymptotic stability of positive periodic solution is derived in terms of M-matrix without any restriction on uniform positiveness of the amplification functions. Comparison and illustrative examples are given to illustrate the effectiveness of the obtained results. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：200 / 212

页数：13

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