Mixed time-delays dependent exponential stability for uncertain stochastic high-order neural networks

被引：17
|
作者：
Zhou Wuneng [1 ,2 ]
Li Minghao [1 ]
机构：
[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201620, Peoples R China
[2] Donghua Univ, Minist Educ, Engn Res Ctr Digitized Text & Fash Technol, Shanghai 201620, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2009年 / 215卷 / 02期
关键词：
Neural networks; Mixed time-delays; Deterministic/Uncertain systems; Exponential stability; Linear matrix inequality (LMI); GLOBAL ASYMPTOTIC STABILITY; H-INFINITY CONTROL; DISTRIBUTED DELAYS; VARYING DELAYS; DISCRETE; SYSTEMS; STABILIZATION;
D O I：
10.1016/j.amc.2009.05.025
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper presents a discrete and distributed time-delays dependent simultaneous approach to deterministic and uncertain stochastic high-order neural networks. New results are proposed in terms of linear matrix inequality (LMI) by exploiting a novel Lyapunov-Krasovskii functional and by making use of novel techniques for time-delay systems. Some constraints of systems are removed, and new results cover some recently published works. Two numerical examples are given to show the usefulness of presented approach. (C) 2009 Elsevier Inc. All rights reserved.
引用
收藏
页码：503 / 513
页数：11
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