Delay-dependent Exponential Stability For a Class of Stochastic Neural Networks With Distributed Delays and Polytopic Uncertainties

被引：0
|
作者：
Xia, Jianwei [1 ]
Meng, Guangwu [1 ]
Wang, Xinhua [1 ]
机构：
[1] Liaocheng Univ, Sch Math Sci, Liaocheng, Shandong, Peoples R China
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS 1-7, CONFERENCE PROCEEDINGS | 2009年
关键词：
stochastic neural networks; polytopic uncertainties; distributed delays; global robust exponential stability; TIME-VARYING DELAYS; GLOBAL ASYMPTOTIC STABILITY; ROBUST STABILITY; INTERVAL; CRITERIA;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The global robust exponential stability in mean square for a class of stochastic neural networks with distributed delays and polytopic uncertainties is investigated in this paper. Parameter-dependent Lypaunov-Krasovskii functionals and free-weighting matrices are employed to obtain sufficient condition that guarantee the robust global exponential stability the considered stochastic neural networks. The derived sufficient conditions are proposed in terms of a set of relaxed linear matrix inequalities(LMIs), which can be checked easily by recently developed algorithms solving LMIs. A numerical example is given to demonstrate the effectiveness of the proposed criteria.
引用
收藏
页码：3118 / 3123
页数：6
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