Projective Synchroniztion of Neural Networks via Continuous/Periodic Event-Based Sampling Algorithms

This study concerns the projective synchronization problem of basic neural networks via continuous/periodic event-based sampling algorithms. Firstly, an event-triggering control scheme is proposed via continuous sampling. In addition, there exists a consistent positive lower bound for the time interval between two successive trigger events, which implies that the Zeno phenomenon will not occur. Next, by designing an appropriate sampling period, a more practical event-triggering scheme is proposed with periodic sampling, which can ensure the projective synchronization of the drive-response neural networks systems. Finally, several examples are elaborated to substantiate the theoretical results.