Intermittent Sampled-Data Synchronization for Space-Varying Reaction-Diffusion Neural Network

The present study investigates the intermittent sampled-data synchronization for a class of space-varying reaction-diffusion neural networks. In the proposed synchronization strategy, the intermittent sampled-data controller is activated only during the work time. We introduce a switching Lyapunov functional respectively related to the work and rest intervals, which is segmented, time-dependent, and continuous in time. By using Lyapunov stability theory, synchronization criterion is proposed to ensure the exponential stability of the synchronization error system. Futhermore control gain is determined, and obtained by solving a set of spatial linear matrix inequalities (SLMIs). Finally, a numerical example is presented to verify the feasibility of the control strategy.