Delay-Dependent Exponential Stability for Uncertain Stochastic Hopfield Neural Networks With Time-Varying Delays

被引：85
|
作者：
Zhang, Baoyong [1 ]
Xu, Shengyuan [1 ]
Zong, Guangdeng [1 ]
Zou, Yun [1 ]
机构：
[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Jiangsu, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS | 2009年 / 56卷 / 06期
基金：
中国国家自然科学基金; 美国国家科学基金会; 中国博士后科学基金;
关键词：
Delay-dependent conditions; Hopfield neural networks; robust exponential stability; stochastic neural networks; time-varying delays; GLOBAL ASYMPTOTIC STABILITY; ROBUST STABILITY; SYSTEMS; CRITERIA; STATE;
D O I：
10.1109/TCSI.2008.2008499
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper provides new delay-dependent conditions that guarantee the robust exponential stability of stochastic Hopfield type neural networks with time-varying delays and parameter uncertainties. Both the cases of the time-varying delays which are differentiable and may not be differentiable are considered. The stability conditions are derived by using the recently developed free-weighting matrices technique and expressed in terms of linear matrix inequalities. Numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria. It is shown that the proposed stability results are less conservative than some previous ones in the literature.
引用
收藏
页码：1241 / 1247
页数：7
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