Improved delay-dependent stability criteria for discrete-time stochastic neural networks with time-varying delays

被引：2
|
作者：
Luo, Meng-zhuo [1 ]
Zhong, Shou-ming [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Math & Sci, Chengdu 611731, Peoples R China
来源：
CEIS 2011 | 2011年 / 15卷
关键词：
Delay-dependent stability; Neural network; Time-varying delay; Lyapunov-Krasovskii; Linear matrix inequalities;
D O I：
10.1016/j.proeng.2011.08.837
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This letter, investigates the problem of mean square exponential stability for a class of discrete-time stochastic neural network with time-varying delays. By constructing a appropriate Lyapunov-Krasovskii functional, combining the stochastic stability theory, and the convex theory method, a delay-dependent exponential stability criteria is obtained in term of LMIs. Finally, a numerical example is exploited to show the usefulness of the results derived.
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页数：5
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