Boundedness and stability for recurrent neural networks with variable coefficients and time-varying delays

被引：51

作者：

Liang, JL ^{[1
]}

Cao, JD ^{[1
]}

机构：

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

来源：

PHYSICS LETTERS A | 2003年 / 318卷 / 1-2期

基金：

中国国家自然科学基金;

关键词：

recurrent neural networks; non-autonomous system; young inequality; ultimate boundedness; lyapunov functional; global exponential stability;

D O I：

10.1016/j.physleta.2003.09.020

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

In this Letter, the problems of boundedness and stability for a general class of non-autonomous recurrent neural networks with variable coefficients and time-varying delays are analyzed via employing Young inequality technique and Lyapunov method. Some simple sufficient conditions are given for boundedness and stability of the solutions for the recurrent neural networks. These results generalize and improve the previous works, and they are easy to check and apply in practice. Two illustrative examples and their numerical simulations are also given to demonstrate the effectiveness of the proposed results. (C) 2003 Elsevier B.V. All rights reserved.

引用

页码：53 / 64

页数：12

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