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

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

Abstract 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...

Full description

Bibliographic Details
Main Authors: Lili Wang, Rui Xu, Zhiqiang Wang
Format: Article
Language:English
Published: SpringerOpen 2017-05-01
Series:Advances in Difference Equations
Subjects:
synchronization
stochastic Cohen-Grossberg neural networks
spacial diffusion
Neumann boundary conditions
periodically intermittent control
Online Access:http://link.springer.com/article/10.1186/s13662-017-1193-3
id doaj-f15471d4059f445a94144e3f3b09336e
record_format Article
spelling doaj-f15471d4059f445a94144e3f3b09336e2020-11-25T00:37:55ZengSpringerOpenAdvances in Difference Equations1687-18472017-05-012017112010.1186/s13662-017-1193-3Synchronization analysis for stochastic reaction-diffusion Cohen-Grossberg neural networks with Neumann boundary conditions via periodically intermittent controlLili Wang0Rui Xu1Zhiqiang Wang2Institute of Applied Mathematics, Shijiazhuang Mechanical Engineering CollegeInstitute of Applied Mathematics, Shijiazhuang Mechanical Engineering CollegeInstitute of Applied Mathematics, Hebei Academy of SciencesAbstract 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.http://link.springer.com/article/10.1186/s13662-017-1193-3synchronizationstochastic Cohen-Grossberg neural networksspacial diffusionNeumann boundary conditionsperiodically intermittent control
collection DOAJ
language English
format Article
sources DOAJ
author Lili Wang
Rui Xu
Zhiqiang Wang
spellingShingle Lili Wang
Rui Xu
Zhiqiang Wang
Synchronization analysis for stochastic reaction-diffusion Cohen-Grossberg neural networks with Neumann boundary conditions via periodically intermittent control
Advances in Difference Equations
synchronization
stochastic Cohen-Grossberg neural networks
spacial diffusion
Neumann boundary conditions
periodically intermittent control
author_facet Lili Wang
Rui Xu
Zhiqiang Wang
author_sort Lili Wang
title Synchronization analysis for stochastic reaction-diffusion Cohen-Grossberg neural networks with Neumann boundary conditions via periodically intermittent control
title_short Synchronization analysis for stochastic reaction-diffusion Cohen-Grossberg neural networks with Neumann boundary conditions via periodically intermittent control
title_full Synchronization analysis for stochastic reaction-diffusion Cohen-Grossberg neural networks with Neumann boundary conditions via periodically intermittent control
title_fullStr Synchronization analysis for stochastic reaction-diffusion Cohen-Grossberg neural networks with Neumann boundary conditions via periodically intermittent control
title_full_unstemmed Synchronization analysis for stochastic reaction-diffusion Cohen-Grossberg neural networks with Neumann boundary conditions via periodically intermittent control
title_sort synchronization analysis for stochastic reaction-diffusion cohen-grossberg neural networks with neumann boundary conditions via periodically intermittent control
publisher SpringerOpen
series Advances in Difference Equations
issn 1687-1847
publishDate 2017-05-01
description Abstract 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.
topic synchronization
stochastic Cohen-Grossberg neural networks
spacial diffusion
Neumann boundary conditions
periodically intermittent control
url http://link.springer.com/article/10.1186/s13662-017-1193-3
work_keys_str_mv AT liliwang synchronizationanalysisforstochasticreactiondiffusioncohengrossbergneuralnetworkswithneumannboundaryconditionsviaperiodicallyintermittentcontrol
AT ruixu synchronizationanalysisforstochasticreactiondiffusioncohengrossbergneuralnetworkswithneumannboundaryconditionsviaperiodicallyintermittentcontrol
AT zhiqiangwang synchronizationanalysisforstochasticreactiondiffusioncohengrossbergneuralnetworkswithneumannboundaryconditionsviaperiodicallyintermittentcontrol
_version_ 1725298971707965440

Similar Items