Hyper-Exponential Stabilization of Neural Networks by Event-Triggered Impulsive Control With Actuation Delay

Abstract

This brief studies the hyper-exponential stabilization of neural networks (NNs) by event-triggered impulsive control, where the impulse instants are determined by the event-triggered conditions. In the presence of actuation delay, an event-triggered impulsive control scheme is devised. For reducing the sampling task of continuous detection, a periodic-detection scheme is also introduced. Within these frameworks, the occurrence of Zeno behavior is rigorously precluded, and some criteria are formulated to achieve the stabilization of the system with a hyper-exponential convergence rate. Moreover, a numerical simulation is provided to elucidate the validity of the theoretical findings.

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