Global Robust Exponential Stability of Uncertain Neutral High-Order Stochastic Hopfield Neural Networks with Time-Varying Delays

被引：22
|
作者：
Gan, Qintao [1 ]
Xu, Rui [1 ]
机构：
[1] Shijiazhuang Mech Engn Coll, Inst Appl Math, Shijiazhuang 050003, Hebei Province, Peoples R China
来源：
NEURAL PROCESSING LETTERS | 2010年 / 32卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Stability; Hopfield neural network; Time-varying delay; Linear matrix inequalities (LMIs); DYNAMIC-ANALYSIS; CRITERIA; SYSTEMS;
D O I：
10.1007/s11063-010-9146-9
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, a class of uncertain neutral high-order stochastic Hopfield neural networks with time-varying delays is investigated. By using Lyapunov-Krasovskii functional and stochastic analysis approaches, new and less conservative delay-dependent stability criteria is presented in terms of linear matrix inequalities to guarantee the neural networks to be globally robustly exponentially stable in the mean square for all admissible parameter uncertainties and stochastic perturbations. Numerical simulations are carried out to illustrate the main results.
引用
收藏
页码：83 / 96
页数：14
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