Synchronization of Stochastic Competitive Neural Networks with Different Timescales and Reaction-Diffusion Terms

We propose a feedback controller for the synchronization of stochastic competitive neural networks with different timescales and reaction-diffusion terms. By constructing a proper Lyapunov-Krasovskii functional, as well as employing stochastic analysis theory, the LaShall-type invariance principle for stochastic differential delay equations, and a linear matrix inequality (LMI) technique, a feedback controller is designed to achieve the asymptotical synchronization of coupled stochastic competitive neural networks. A simulation example is given to show the effectiveness of the theoretical results.