Absolute exponential stability of a class of continuous-time recurrent neural networks

被引：54

作者：

Hu, SQ ^{[1
]}

Wang, J ^{[1
]}

机构：

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

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2003年 / 14卷 / 01期

关键词：

absolute exponential stability (AEST); additive diagonal stability; diagonal semistability; global exponential stability; H-matrix; neural networks;

D O I：

10.1109/TNN.2002.806954

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a new result on absolute exponential stability (AEST) of a class of continuous-time recurrent neural networks with locally Lipschitz continuous and monotone nondecreasing activation functions. The additively diagonally stable connection weight matrices Are proven to be able to guarantee AEST of the neural networks. The AEST result extends and improves the existing absolute stability and AEST ones in the literature.

引用

页码：35 / 45

页数：11

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