Global exponential stability and periodicity of recurrent neural networks with time delays

被引：279

作者：

Cao, JD ^{[1
]}

Wang, J

机构：

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

[2] Chinese Univ Hong Kong, Dept Automat & Comp Aided Engn, Hong Kong, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS | 2005年 / 52卷 / 05期

基金：

中国国家自然科学基金;

关键词：

inequality; Lyapunov method; periodicity; recurrent neural networks; stability; time delay;

D O I：

10.1109/TCSI.2005.846211

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, the global exponential stability and periodicity of a class of recurrent neural networks with time delays are addressed by using Lyapunov functional method and inequality techniques. The delayed neural network includes the well-known Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks as its special cases. New criteria are found to ascertain the global exponential stability and periodicity of the recurrent neural networks with time delays, and are also shown to be different from and improve upon existing ones.

引用

页码：920 / 931

页数：12

共 34 条

[1] On the global asymptotic stability of delayed cellular neural networks [J].