Ultimate Boundedness Of Stochastic Neural Networks With Delays

被引：0

作者：

Wan, Li ^{[1
]}

Zhou, Qinghua ^{[2
]}

机构：

[1] Wuhan Text Univ, Dept Math & Phys, Wuhan 430073, Peoples R China

[2] Zhaoqing Univ, Dept Math, Zhaoqing PT-526061, Peoples R China

来源：

ADVANCED RESEARCH ON INDUSTRY, INFORMATION SYSTEMS AND MATERIAL ENGINEERING, PTS 1-7 | 2011年 / 204-210卷

关键词：

Stochastic neural networks; Ultimate boundedness; Delays; SURE EXPONENTIAL STABILITY; TIME-VARYING DELAYS; DISTRIBUTED DELAYS; GLOBAL STABILITY;

D O I：

10.4028/www.scientific.net/AMR.204-210.1549

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Although ultimate boundedness of several classes of neural networks with constant delays was studied by some researchers, the inherent randomness associated with signal transmission was not taken account into these networks. At present, few authors study ultimate boundedness of stochastic neural networks and no related papers are reported. In this paper, by using Lyapunov functional and linear matrix inequality, some sufficient conditions ensuring the ultimate boundedness of stochastic neural networks with time-varying delays are established. Our criteria are easily tested by Matlab LMI Toolbox. One example is given to demonstrate our criteria.

引用

页码：1549 / +

页数：2

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