Robust passivity analysis of a class of discrete-time stochastic neural networks

被引：5
|
作者：
Shi, Guodong [1 ]
Ma, Qian [2 ]
Qu, Yi [2 ]
机构：
[1] Changzhou Univ, Sch Informat Sci & Engn, Changzhou 213164, Peoples R China
[2] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Jiangsu, Peoples R China
来源：
NEURAL COMPUTING & APPLICATIONS | 2013年 / 22卷 / 7-8期
关键词：
Discrete-time; Passivity; Stochastic neural networks; Linear matrix inequality; DEPENDENT EXPONENTIAL STABILITY; VARYING DELAYS; NEUTRAL-TYPE; CRITERIA;
D O I：
10.1007/s00521-012-0838-0
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 addresses the passivity problem of a class of discrete-time stochastic neural networks with time-varying delays and norm-bounded parameter uncertainties. New delay-dependent passivity conditions are obtained by using a novel Lyapunov functional together with the linear matrix inequality approach. Numerical examples show the effectiveness of the proposed method.
引用
收藏
页码：1509 / 1517
页数：9
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