Improved Delay-dependent Robust Stability Analysis for Neutral-type Uncertain Neural Networks with Markovian jumping Parameters and Time-varying Delays

被引：43

作者：

Xia, Jianwei ^{[1
,2
]}

Park, Ju H. ^{[2
]}

Zeng, Hongbing ^{[3
]}

机构：

[1] Liaocheng Univ, Sch Math Sci, Liaocheng 252000, Shandong, Peoples R China

[2] Yeungnam Univ, Dept Elect Engn, Kyongsan 712749, South Korea

[3] Hunan Univ Technol, Sch Elect & Informat Engn, Zhuzhou 412007, Peoples R China

来源：

NEUROCOMPUTING | 2015年 / 149卷

基金：

新加坡国家研究基金会; 中国国家自然科学基金;

关键词：

Neutral-type neural networks; Stochastic stability; Markovian jumping parameters; Time-varying delays; LMIs; GLOBAL ASYMPTOTIC STABILITY; H-INFINITY CONTROL; EXPONENTIAL STABILITY; SYSTEMS; DISCRETE; CRITERIA;

D O I：

10.1016/j.neucom.2014.09.008

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 deals with the problem of robust stochastic stability analysis for a class of neutral-type uncertain neural networks with Markovian jumping parameters and time-varying delays. By introducing an novel mode-dependent Augmented Lyapunov-Krasovskii functional with delay partitioning and Wirtinger-based integral inequality techniques, some improved delay-dependent stochastically stable conditions are proposed in the form of LMIs. Numerical simulations are provided to show the effectiveness and less conservatism of the results. (c) 2014 Elsevier B.V. All rights reserved.

引用

页码：1198 / 1205

页数：8

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