Global asymptotic stability and global exponential stability of continuous-time recurrent neural networks

被引：63

作者：

Hu, SQ ^{[1
]}

Wang, J ^{[1
]}

机构：

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

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2002年 / 47卷 / 05期

关键词：

global asymptotic (exponential) stability; Lipschitz continuous; recurrent neural networks;

D O I：

10.1109/TAC.2002.1000277

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This note presents new results on global asymptotic stability (GAS) and global exponential stability (GES) of a general class of continuous-time recurrent neural networks with Lipschitz continuous and monotone nondecreasing activation functions. We first give three sufficient conditions for the GAS of neural networks. These testable sufficient conditions differ from and improve upon existing ones. We then extend an existing GAS result to GES one and also extend the existing GES results to more general cases with less restrictive connection weight matrices and/or partially Lipschitz activation functions.

引用

页码：802 / 807

页数：6

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