Exponential estimates and exponential stability for neutral-type neural networks with multiple delays

被引：25
|
作者：
Liao, Xiaofeng [1 ,2 ]
Liu, Yilu [1 ,2 ]
Wang, Huiwei [1 ,2 ]
Huang, Tingwen [3 ]
机构：
[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China
[2] Chongqing Univ, Coll Comp, Chongqing 630044, Peoples R China
[3] Texas A&M Univ, Doha, Qatar
来源：
NEUROCOMPUTING | 2015年 / 149卷
基金：
中国国家自然科学基金;
关键词：
Neutral-type neural networks; Exponential estimate; Exponential stability; Lyapunov-Krasovskii functional; Linear matrix inequalities; Descriptor transformation; TIME DELAYS; DIFFERENTIAL-EQUATIONS; ASYMPTOTIC STABILITY; GLOBAL STABILITY; SYSTEMS; CRITERIA; CIRCUIT;
D O I：
10.1016/j.neucom.2014.07.048
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, exponential estimates and sufficient criteria for the exponential stability of neutral-type neural networks with multiple delays are given. First, because of the key role of the difference equation part of the neutral-type neural networks with multiple delays, some novel results concerning exponential estimates for non-homogeneous difference equations evolving in continuous time are derived. Then, by constructing several different Lyapunov-Krasovskii functionals combined with a descriptor transformation approach at some cases, several novel global and exponential stability conditions are presented and expressed in terms of linear matrix inequalities (LMIs), and the obtained results are less conservative and restrictive than the known results. Some numerical examples are also given to show their effectiveness and advantages over others. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：868 / 883
页数：16
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