Global Mean Square Exponential Stability of Memristor-Based Stochastic Neural Networks with Time-Varying Delays

被引：0
|
作者：
Xu, Xiao-Lin [1 ]
机构：
[1] Hubei Normal Univ, Coll Comp Sci & Technol, Huangshi 435002, Hubei, Peoples R China
来源：
CURRENT TRENDS IN COMPUTER SCIENCE AND MECHANICAL AUTOMATION, VOL 1 | 2017年
关键词：
memristor-based neural networks; stochastic systems; mean square exponential stability; nonsmooth analysis; SYNCHRONIZATION;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, we study the global mean square exponential stability of memristor-based stochastic neural networks with time-varying delays by the means of Lyapunov function and ito formula. Meanwhile, one of the central ideas of this paper is that the theory of differential equations about discontinuous right-hand sides is applied. The proposed exponential stability criteria extend and improve some existing works. A numerical example is given to verify the results.
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页码：270 / 279
页数：10
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