Asymptotic stability analysis of stochastic reaction-diffusion Cohen-Grossberg neural networks with mixed time delays

In this paper, the asymptotic stability problem is studied for a class of stochastic Cohen-Grossberg neural networks with reaction-diffusion and time-mixed delays. By using the Lyapunov-Krasovskii functional, stochastic analysis technology and linear matrix inequalities (LMIs) technique, several sufficient conditions on the asymptotic stability for the considered system are obtained. The condition not only connects with the delays and diffusion effect, but also relates to the magnitude of noise. Therefore, these stability criteria are essentially new and more effective than those given in previous conditions. Two examples are presented to illustrate the effectiveness and efficiency of the results.