Decentralized Adaptive Event-Triggered Synchronization of Neutral Neural Networks with Time-Varying Delays

In this work, the adaptive event-triggered synchronization in a class of master-slave neutral neural networks with time-varying delay is studied. The design of decentralized event-triggered scheme is proposed, which only utilizes local available information to determine the released instants from multiple sensors to a centralized controller. Different from existing ones, the triggering thresholds depend on real-time performance of controlled system. Together with some novel Lyapunov terms, an augmented Lyapunov-Krasovskii functional is constructed, in which the interconnection between time delays can be fully utilized. In particular, a less conservative condition on controller gain is obtained in terms of linear matrix inequalities. Finally, the derived results are verified by resorting to two numerical examples.