Stability analysis of time varying delayed stochastic Hopfield neural networks in numerical simulation

被引：8

作者：

Liu, Linna ^{[1
]}

Deng, Feiqi ^{[1
]}

机构：

[1] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China

来源：

NEUROCOMPUTING | 2018年 / 316卷

基金：

中国国家自然科学基金;

关键词：

Stochastic differential equation; Hopfield neural network; Time delay; Numerical simulation; Stability; MEAN-SQUARE STABILITY; EXPONENTIAL STABILITY; ROBUST STABILITY;

D O I：

10.1016/j.neucom.2018.08.004

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 is concerned with the stability analysis of time varying delayed stochastic Hopfield neural networks in numerical simulation . To achieve our expected conclusions, we will reform the classical contractive mapping principle in functional analysis, with some modifications, to adapt to our conditions and both the continuous and the discrete delayed models. Under the reasonable conditions, it is shown that, the Euler-Maruyama numerical scheme is mean square exponentially stable of exact solution dependent of step size. Further more, it is also shown that the backward Euler-Maruyama numerical scheme can share the mean square exponential stability of the exact solution independent of step size under the same conditions. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：294 / 305

页数：12

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