Boundedness and global convergence of non-autonomous neural networks with variable delays

被引：10

作者：

Yuan, Zhaohui ^{[1
]}

Yuan, Lifen ^{[2
]}

Huang, Lihong ^{[1
]}

Hu, Dewen ^{[3
]}

机构：

[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China

[2] Hunan Normal Univ, Coll Informat Sci, Changsha 410081, Hunan, Peoples R China

[3] Natl Univ Def Technol, Coll Mechatron & Automat, Changsha 410073, Hunan, Peoples R China

来源：

NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS | 2009年 / 10卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Boundedness; Convergence; Delay; Neural networks; Asymptotic system; TIME-VARYING DELAYS; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; PERIODIC-SOLUTIONS; EXISTENCE; FEEDBACK; SYSTEMS;

D O I：

10.1016/j.nonrwa.2008.04.004

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper is concerned with the boundedness and global convergence of the solutions of a non-autonomous system with variable delays, arising from the description of the states of neurons in delayed Hopfield neural networks in a time-varying situation. By using the analysis method, inequality technique and the properties of M-matrix, several novel sufficient conditions ensuring the boundedness and global convergence of all solutions are established. Our results are new and complement previously known results. The theoretical analysis is verified by numerical simulations. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.

引用

页码：2195 / 2206

页数：12

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