Graph-theoretic approach to exponential synchronization of stochastic reaction–diffusion Cohen–Grossberg neural networks with time-varying delays

A stochastic reaction–diffusion Cohen–Grossberg neural network (CGNN) with time-varying delays is concerned. In the model, time delay effects, diffusion effects and the white noise are taken into account at the same time. Based on graph theory and Lyapunov method, the sufficient conditions for exponential synchronization are considered. This method is different from the traditional methods. Two different types of sufficient criteria for synchronization are presented in the form of Lyapunov functions and the coefficients of drive-response network9s, respectively. They both reveal the relationship between exponential synchronization and the topology structure of the systems. Finally, several numerical simulation figures are illustrated to show the effectiveness of the obtained results.