Exponential synchronization of Cohen-Grossberg neural networks with stochastic perturbation and reaction-diffusion terms via periodically intermittent control

Lili Wang, Rui Xu

Abstract


In this paper, a class of Cohen-Grossberg neural networks with mixed time-varying delays, stochastic perturbation and reaction-diffusion terms is investigated. The exponential synchronization criteria in terms of p-norm are obtained based on periodically intermittent control by means of Lyapunov functional theory, mathematical induction and inequality technique. The influences of stochastic perturbation, spacial diffusion, the control rate and the control strength on the exponential synchronization are discussed according to the obtained synchronization criteria. The proposed criteria improve the previous known results in the literature and remove the restrictions on the mixed time-varying delays. Numerical simulations are carried out to illustrate the feasibility of the results.

https://doi.org/10.28919/cmbn/2853


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Published: 2017-03-02

How to Cite this Article:

Lili Wang, Rui Xu, Exponential synchronization of Cohen-Grossberg neural networks with stochastic perturbation and reaction-diffusion terms via periodically intermittent control, Communications in Mathematical Biology and Neuroscience, Vol 2017 (2017), Article ID 6

Copyright © 2017 Lili Wang, Rui Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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