Intermittent Finite-Time Synchronization for Reaction-Diffusion Competitive Neural Networks with Different Time Scales

This paper focuses on the finite-time synchronization issue for reaction-diffusion competitive neural networks (RCNNs) with different time scales and time-varying delays. To reduce the waste of network resources, a periodically intermittent control strategy is presented based on two time scales (short and long memory) and time-varying delay. By constructing the Lyapunov-Krasovskii functional, with the help of Lyapunov stability theory and auxiliary inequality technique, the finite-time synchronization can be guaranteed and the settling time is exactly estimated. Finally, an exhaustive numerical analysis is presented to illustrate the effectiveness of the controller and the correctness of theoretical results.