Fixed points and pth moment exponential stability of stochastic delayed recurrent neural networks with impulses

被引：14

作者：

Chen, Guiling ^{[1
,2
]}

van Gaans, Onno ^{[1
]}

Lunel, Sjoerd Verduyn ^{[3
]}

机构：

[1] Leiden Univ, Inst Math, NL-2300 RA Leiden, Netherlands

[2] Southwest Jiaotong Univ, Dept Math, Chengdu 610031, Peoples R China

[3] Univ Utrecht, Inst Math, NL-3508 TA Utrecht, Netherlands

来源：

APPLIED MATHEMATICS LETTERS | 2014年 / 27卷

关键词：

Fixed point theory; Exponential stability; Stochastic delayed neural networks; Variable delays; Impulses; TIME-VARYING DELAYS; ASYMPTOTIC STABILITY; DIFFERENTIAL-EQUATIONS;

D O I：

10.1016/j.aml.2013.08.005

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

New sufficient conditions for pth moment exponential stability of a class of impulsive stochastic delayed recurrent neural networks are presented by using fixed point theory. Our results neither require the boundedness, monotonicity and differentiability of the activation functions nor differentiability of the time varying delays. A class of impulsive delayed neural networks without stochastic perturbations are also considered. An example is given to illustrate our main results. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：36 / 42

页数：7

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