Global Passivity of Stochastic Neural Networks with Time-Varying Delays

被引：0

作者：

Liang, Jinming ^{[1
]}

Song, Qiankun ^{[2
]}

机构：

[1] Sichuan Univ Sci & Engn, Sch Comp Sci, Chengdu 643000, Sichuan, Peoples R China

[2] Chongqing Jiaotong Univ, Dept Math, Chongqing 400074, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 1, PROCEEDINGS | 2009年 / 5551卷

基金：

中国国家自然科学基金;

关键词：

Passivity; Stochastic neural networks; Time-varying delays; EXPONENTIAL STABILITY; DISTRIBUTED DELAYS; DISCRETE; PASSIFICATION; SYSTEMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the passivity problem is investigated for a class of stochastic neural networks with time-varying delays as well as generalized activation functions. By empolying a combination of Lyapunov functional, the free-weighting matrix method and stochastic analysis technique, a delay-independent criterion for the passivity of the addressed neural networks is established in terms of linear matrix inequalities (LMIs), which can he checked numerically using the effective LMI toolbox in MATLAB. An example is given to show the effectiveness and less conservatism of the proposed criterion. It is noteworthy that the traditional assumptions on the differentiability of the time-varying delays and the boundedness of its derivative are removed.

引用

页码：405 / +

页数：3

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