Robust global exponential stability of Cohen-Grossberg neural networks with time delays

被引：153

作者：

Chen, TP ^{[1
]}

Rong, LB ^{[1
]}

机构：

[1] Fudan Univ, Inst Math, Lab Nonlinear Sci, Shanghai 200433, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2004年 / 15卷 / 01期

关键词：

global exponential convergence; M-matrix; neural networks; stability; time delay;

D O I：

10.1109/TNN.2003.822974

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this letter, we discuss delayed Cohen-Grossberg neural network models and investigate their global exponential stability of the equilibrium point for the systems. A set of sufficient conditions ensuring robust global exponential convergence of the Cohen-Grossberg neural networks with time delays are given.

引用

页码：203 / 206

页数：4

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