Synchronization analysis for stochastic reaction-diffusion Cohen-Grossberg neural networks with Neumann boundary conditions via periodically intermittent control

Advances in Difference Equations

Zhiqiang Wang¹  Rui Xu²  Lili Wang² 

[1] Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, P.R. China;Institute of Applied Mathematics, Shijiazhuang Mechanical Engineering College, Shijiazhuang, P.R. China

关键词: synchronization;    stochastic Cohen-Grossberg neural networks;    spacial diffusion;    Neumann boundary conditions;    periodically intermittent control;   

DOI  :  10.1186/s13662-017-1193-3

学科分类：数学（综合）

来源: SpringerOpen

【 摘 要 】

In this paper, mathematical analysis is proposed on the synchronization problem for stochastic reaction-diffusion Cohen-Grossberg neural networks with Neumann boundary conditions. By introducing several important inequalities and using Lyapunov functional technique, some new synchronization criteria in terms of p-norm are derived under periodically intermittent control. Some previous known results in the literature are improved, and some restrictions on the mixed time-varying delays are removed. The influence of diffusion coefficients, diffusion space, stochastic perturbation and control width on synchronization is analyzed by the obtained synchronization criteria. Numerical simulations are presented to show the feasibility of the theoretical results.

【 授权许可】

CC BY