Global exponential synchronization of generalized stochastic neural networks with mixed time-varying delays and reaction-diffusion terms

This paper investigates the synchronization problem of generalized stochastic neural networks with mixed time-varying delays and reaction-diffusion terms using linear feedback control. Lyapunov stability theory combining with stochastic analysis approaches is employed to derive sufficient criteria ensuring the coupled chaotic generalized stochastic neural networks to be globally exponentially synchronized. The generalized neural networks model considered includes reaction-diffusion Hopfield neural networks, reaction-diffusion bidirectional associative memory neural networks, and reaction-diffusion cellular neural networks as its special cases. It is theoretically proven that these synchronization criteria are more effective than some existing ones. This paper also presents some illustrative examples and uses simulated results of these examples to show the feasibility and effectiveness of the proposed scheme.