Stochastic stability analysis for neural networks with mixed time-varying delays

被引：4
|
作者：
Ma, Yuechao [1 ]
Zheng, Yuqing [1 ]
机构：
[1] Yanshan Univ, Coll Sci, Qinhuangdao, Hebei Province, Peoples R China
来源：
NEURAL COMPUTING & APPLICATIONS | 2015年 / 26卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Stochastic stability; Markovian jumping; Discrete time delays; Distributed time delays; Lyapunov-Krasovskii functional; GLOBAL ASYMPTOTIC STABILITY; MARKOVIAN JUMPING PARAMETERS; UNBOUNDED DISTRIBUTED DELAYS; DEPENDENT ROBUST STABILITY; EXPONENTIAL STABILITY; NEUTRAL SYSTEMS; DISCRETE; CONTROLLABILITY; STABILIZABILITY; CRITERION;
D O I：
10.1007/s00521-014-1735-5
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 problem of the stochastic stability analysis for Markovian jumping neural networks with time-varying delays and stochastic perturbation. Some criteria for the stability and robust stability of such neural networks are derived, by means of constructing suitable Lyapunov-Krasovskii functionals and a unified linear matrix inequality (LMI) approach. Note that the LMIs can be easily solved by using the Matlab LMI toolbox and no tuning of parameters is required. Finally, numerical examples are used to illustrate the effectiveness and advantage of the proposed techniques.
引用
收藏
页码：447 / 455
页数：9
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