Event-Triggered Synchronization for Memristor-Based Neural Networks

Abstract

This paper studies the synchronization problem of memristor-based neural networks with networked environment, in which the network bandwidth and computational resources are limited. An event-triggered state feedback control strategy is proposed. By taking the proposed triggering mechanism into account, some criteria are developed such that memristor-based neural networks achieve synchronization with limited network bandwidth and computational resource. Different from the previous related works, continuous event detectors are adopted to determine when to broadcast synchronized error information to control input, and the Zeno phenomena are considered. Finally, a numerical simulation example is given to verify the effectiveness of the proposed event-triggered control strategy.