Stability of stochastic delay neural networks

被引：345

作者：

Blythe, S ^{[1
]}

Mao, XR ^{[1
]}

Liao, XX ^{[1
]}

机构：

[1] Univ Strathclyde, Dept Stat & Modelling Sci, Glasgow G1 1XH, Lanark, Scotland

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2001年 / 338卷 / 04期

基金：

英国生物技术与生命科学研究理事会; 英国工程与自然科学研究理事会;

关键词：

delay neural network; Brownian motion; martingale convergence theorem; lyapunov; exponent; exponential stability;

D O I：

10.1016/S0016-0032(01)00016-3

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The authors in their papers (Liao and Mao, Stochast. Anal. Appl. 14 (2) (1996a) 165-185; Neural, Parallel Sci. Comput. 4 (2) (1996b) 205-244) initiated the study of stability and instability of stochastic neural networks and this paper is the continuation of their research in this area. The main aim of this paper is to discuss almost sure exponential stability for a stochastic delay neural network dx(t) = [-Bx(t) + Ag(x(tau)(t))] dt + sigma (x(t), g(x(tau)(t), t) dw(t). The techniques used in this paper are different from those in their earlier papers. Especially, the nonnegative semimartingale convergence theorem will play an important role in this paper. Several examples are also given for illustration. (C) 2001 The Franklin Institute. Published by Elsevier Science Ltd. All rights reserved.

引用

页码：481 / 495

页数：15

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