Global output convergence of Hopfield neural networks with time-varying thresholds

被引：0

作者：

Wu, W ^{[1
]}

Huang, M ^{[1
]}

Cui, BT ^{[1
]}

机构：

[1] So Yangtze Univ, Res Ctr Control Sci & Engn, Jiangsu 214122, Peoples R China

来源：

DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES A-MATHEMATICAL ANALYSIS | 2006年 / 13卷

关键词：

neural network; global stability; time-varying threshholds;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper studies the global output convergence of a class of Hopfield neural networks with globally Lipschitz continuous and monotone nondecreasing activation functions. We establish two sufficient conditions for global output convergence of this class of neural networks. Symmetry in the connection weight matrix is not required in the present results which extend the existing ones.

引用

页码：348 / 355

页数：8

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