Event-Triggered Regulation for Enhanced Synchronization of Recurrent Neural Networks using Time-Varying Delay

Abstract

Abstract This research is primarily concerned having the synchronization issue of time-varying delay recurrent neural networks (RNNs) with event-triggered regulation (ETR). ETR may legitimately reduce the number of control activities performed and the amount of energy expended. Via the L-K functional, Schur complement lemma, linear matrix inequalities (LMIs), and other analytic techniques, a sufficient condition for the synchronization of the drive-response system is achieved. Consequently, a more sensible and effective controller is implemented. Using a Matlab-made example and graph, we can show that our inferred results are correct and we can ensure the rationality of the set controller.