Adaptive almost surely asymptotically synchronization for stochastic delayed neural networks with Markovian switching

Advances in Difference Equations

Xiangwu Ding¹  Wuneng Zhou²  Yan Gao²  Dongbing Tong²  Hongye Su³ 

[1] College of Computer Sciences and Technology, Donghua University, Shanghai, P.R. China;College of Information Science and Technology, Donghua University, Shanghai, P.R. China;The National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, P.R. China

关键词: neutral networks;    adaptive almost surely asymptotically synchronization;    Markovian switching;    stochastic noise;    time-varying delays;   

DOI  :  10.1186/1687-1847-2013-211

学科分类：数学（综合）

来源: SpringerOpen

【 摘 要 】

In this paper, the problem of the adaptive almost surely asymptotically synchronization for stochastic delayed neural networks with Markovian switching is considered. By utilizing a new nonnegative function and the M-matrix approach, we derive a sufficient condition to ensure adaptive almost surely asymptotically synchronization for stochastic delayed neural networks. Some appropriate parameters analysis and update laws are found via the adaptive feedback control techniques. We also present an illustrative numerical example to demonstrate the effectiveness of the M-matrix-based synchronization condition derived in this paper.

【 授权许可】

CC BY