Global exponential stability in Lagrange sense for recurrent neural networks with time delays

被引：88

作者：

Liao, Xiaoxin ^{[3
]}

Luo, Qi ^{[4
]}

Zeng, Zhigang ^{[1
]}

Guo, Yunxia ^{[2
]}

机构：

[1] Wuhan Univ Technol, Sch Automat, Wuhan 430070, Peoples R China

[2] Zhuhai Radio & TV Univ, Zhuhai 519000, Guangdong, Peoples R China

[3] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Hubei, Peoples R China

[4] Nanjing Univ Informat Sci & Technol, Dept Informat & Commun, Nanjing 210044, Jiangsu, Peoples R China

来源：

NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS | 2008年 / 9卷 / 04期

关键词：

recurrent neural networks; Lagrange stability; global exponential attractivity; delays;

D O I：

10.1016/j.nonrwa.2007.03.018

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we study the global exponential stability in Lagrange sense for continuous recurrent neural networks (RNNs) with multiple time delays. Three different types of activation functions are considered, which include both bounded and unbounded activation functions. By constructing appropriate Lyapunov-like functions, we provide easily verifiable criteria for the boundedness and global exponential attractivity of RNNs. These results can be applied to analyze monostable as well as multistable neural networks. (C) 2007 Elsevier Ltd. All rights reserved.

引用

页码：1535 / 1557

页数：23

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