An improved algebraic criterion for global exponential stability of recurrent neural networks with time-varying delays

被引：107

作者：

Shen, Yi ^{[1
]}

Wang, Jun ^{[2
]}

机构：

[1] Huazhong Univ Sci & Technol, Key Lab Minist Educ Image Proc & Intelligent Cont, Wuhan 430074, Hubei, Peoples R China

[2] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, New Territories, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2008年 / 19卷 / 03期

关键词：

global exponential stability; M-matrix; recurrent neural networks; time-varying delays;

D O I：

10.1109/TNN.2007.911751

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This brief paper presents an M-matrix-based algebraic criterion for the global exponential stability of a class of recurrent neural networks with decreasing time-varying delays. The criterion improves some previous criteria based on M-matrix and is easy to be verified with the connection weights of the recurrent neural networks with decreasing time-varying delays. In addition, the rate of exponential convergence can be estimated via a simple computation based on the criterion herein.

引用

页码：528 / 531

页数：4

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