Global exponential periodicity of a class of neural networks with recent-history distributed delays

被引：13

作者：

Yang, XF ^{[1
]}

Liao, XF

Megson, GM

Evans, DJ

机构：

[1] Chongqing Univ, Dept Comp Sci & Engn, Chongqing 400044, Peoples R China

[2] Univ Reading, Sch Syst Engn, Dept Comp Sci, Reading RG6 6AY, Berks, England

[3] Univ Loughborough, Dept Comp Sci, Algorithms & Architectures Res Ctr, Loughborough LE11 3TU, Leics, England

来源：

CHAOS SOLITONS & FRACTALS | 2005年 / 25卷 / 02期

关键词：

D O I：

10.1016/j.chaos.2004.11.014

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, we propose to study a class of neural networks with recent-history distributed delays. A sufficient condition is derived for the global exponential periodicity of the proposed neural networks, which has the advantage that it assumes neither the differentiability nor monotonicity of the activation function of each neuron nor the symmetry of the feedback matrix or delayed feedback matrix. Our criterion is shown to be valid by applying it to an illustrative system. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：441 / 447

页数：7

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