Synchronization of a class of uncertain stochastic discrete-time delayed neural networks

Advances in Difference Equations

Jianming Lin¹  Bing Xiao²  Zhong Chen³ 

[1] School of Economic and Management, Guangzhou University of Chinese Medicine, Guangzhou, China;School of Mathematical Sciences, Xinjiang Normal University, Urumqi, China;School of Mathematics and Information Sciences, Shaoguan University, Shaoguan, China

关键词: synchronization;    discrete-time neural networks;    time-varying delays;    distributed delays;    Lyapunov functional method;    stochastic delayed dynamical system;   

DOI  :  10.1186/1687-1847-2014-212

学科分类：数学（综合）

来源: SpringerOpen

【 摘 要 】

The global asymptotical synchronization problem is discussed for a general class of uncertain stochastic discrete-time neural networks with time delay in this paper. Time delays include time-varying delay and distributed delay. Based on the drive-response concept and the Lyapunov stability theorem, a linear matrix inequality (LMI) approach is given to establish sufficient conditions under which the considered neural networks are globally asymptotically synchronized in the mean square. Therefore, the global asymptotical synchronization of the stochastic discrete-time neural networks can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Moreover, the obtained results are dependent not only on the lower bound but also on the upper bound of the time-varying delays, that is, they are delay-dependent. And finally, a simulation example is given to illustrate the effectiveness of the proposed synchronization scheme.

【 授权许可】

CC BY