Stability analysis of stochastic neural networks with Markovian jump parameters using delay-partitioning approach

被引：23
作者：
Chen, Weimin [1 ,2 ]
Ma, Qian [2 ]
Miao, Guoying [2 ]
Zhang, Yijun [2 ]
机构：
[1] Nanjing Univ Sci & Technol, Dept Appl Math, Nanjing 210094, Jiangsu, Peoples R China
[2] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Jiangsu, Peoples R China
来源：
NEUROCOMPUTING | 2013年 / 103卷
基金：
美国国家科学基金会;
关键词：
Asymptotic stability; Time-varying delays; Delay-partitioning; Stochastic neural networks; ROBUST EXPONENTIAL STABILITY; DEPENDENT ASYMPTOTIC STABILITY; DISCRETE; SYSTEMS; CRITERIA;
D O I：
10.1016/j.neucom.2012.04.030
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 problem of mean square asymptotic stability of stochastic neural networks with Markovian jumping parameters is considered. By choosing an augmented Lyapunov-Krasovskii functional and utilizing the delay-partitioning method, novel delay-dependent mean square asymptotic stability conditions are derived in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness of the proposed approach. (c) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：22 / 28
页数：7
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