Delay-probability-distribution-dependent stability criteria for discrete-time stochastic neural networks with random delays

Advances in Difference Equations

Shouming Zhong¹  Yong Ren²  Xia Zhou³ 

[1] College of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, P.R. China;Department of Mathematics, Anhui Normal University, Wuhu, P.R. China;School of Mathematic and Computational Science, Fuyang Teachers College, Fuyang, P.R. China

关键词: discrete-time stochastic neural networks;    discrete time-varying delays;    delay-probability-distribution-dependent;    robust exponential stability;    LMIs;   

DOI  :  10.1186/1687-1847-2013-314

学科分类：数学（综合）

来源: SpringerOpen

【 摘 要 】

The problem of delay-probability-distribution-dependent robust stability for a class of discrete-time stochastic neural networks (DSNNs) with delayed and parameter uncertainties is investigated. The information of the probability distribution of the delay is considered and transformed into parameter matrices of the transferred DSSN model. In the DSSN model, the time-varying delay is characterized by introducing a Bernoulli stochastic variable. By constructing an augmented Lyapunov-Krasovskii functional and introducing some analysis techniques, some novel delay-distribution-dependent mean square stability conditions for the DSSN, which are to be robustly globally exponentially stable, are derived. Finally, a numerical example is provided to demonstrate less conservatism and effectiveness of the proposed methods.

【 授权许可】

CC BY