Synchronization for hybrid coupled reaction-diffusion neural networks with stochastic disturbances via spatial sampled-data control strategy

Abstract

The synchronization of reaction-diffusion neural networks with state and spatial couplings is investigated in this article, and the time-varying delay and stochastic disturbances are considered in the proposed systems. Due to the development and merits of digital controllers, sampled-data control is a natural choice to establish synchronization in continuous-time systems. Here, we suggest a spatial sampled-data controller design, where the sampled-data (in space) measurements of the state are taken in a finite number of fixed sampling points in the spatial domain. It is assumed that the sampling intervals in space are bounded. Based on the Lyapunov stability theory, Young’s and Wirtinger’s inequalities techniques, some sufficient conditions are presented to synchronize the hybrid coupling reaction-diffusion neural networks with stochastic disturbances. Finally, the efficiency of the derived criteria will be demonstrated by resorting to two numerical examples.