Delay-dependent exponential stability for uncertain neutral stochastic neural networks with interval time-varying delay

被引：23
|
作者：
Chen, Huabin [1 ]
Zhao, Yang [1 ,2 ]
机构：
[1] Nanchang Univ, Dept Math, Nanchang, Peoples R China
[2] Beijing Univ Technol, Coll Appl Sci, Dept Probabil & Stat, Beijing, Peoples R China
来源：
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE | 2015年 / 46卷 / 14期
基金：
中国国家自然科学基金;
关键词：
neutral stochastic neural networks; exponential stability; linear matrix inequalities (LMIs); interval time-varying delay; Lyapunov-Krasovskii functional (LKF); GLOBAL ASYMPTOTIC STABILITY; ROBUST STABILITY; NONLINEAR-SYSTEMS; CRITERIA; DISCRETE; STABILIZATION; DESIGN;
D O I：
10.1080/00207721.2013.874507
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper is mainly concerned with the problem for the robustly exponential stability in mean square moment of uncertain neutral stochastic neural networks with interval time-varying delay. With an appropriate augmented Lyapunov-Krasovskii functional (LKF) formulated, the convex combination method is utilised to estimate the derivative of the LKF. Some new delay-dependent exponential stability criteria for such systems are obtained in terms of linear matrix inequalities, which involve fewer matrix variables and have less conservatism. Finally, two illustrative numerical examples are given to show the effectiveness of our obtained results.
引用
收藏
页码：2584 / 2597
页数：14
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