Global exponential stability for recurrent neural networks with a general class of activation functions and variable delays

被引：0

作者：

Zhou, DM ^{[1
]}

Zhang, LM ^{[1
]}

Zhao, DF ^{[1
]}

机构：

[1] Yunnan Univ, Informat Coll, Kunming 650091, Peoples R China

来源：

PROCEEDINGS OF 2003 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS & SIGNAL PROCESSING, PROCEEDINGS, VOLS 1 AND 2 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Based on a general class of activation functions, new results guaranteeing the global exponential stability of the equilibrium for a class of recurrent neural networks with variable delays are obtained, The delayed Hopfield neural network and bidirectional associative memory network and cellular neural networks are special cases of the network model considered in this paper. In addition, we do not require the activation functions to be differentiable, bounded and monotone nondecreasing. So this work gives some improvements to the previous ones.

引用

页码：108 / 111

页数：4

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