Preset-Time Synchronization for Memristive Neural Networks Under Intermittent Event-Triggered Control

This paper investigates the preset-time synchronization (PTS) problem for memristive neural networks (MNNs) by introducing a preset-time gain function and designing an intermittent dynamic event-triggered strategy (IE-TCS). First, based on a nominal system, an IE-TCS is developed, and the exponential synchronization of this nominal system is rigorously proved using the proposed intermittent dynamic event-triggered control scheme combined with Lyapunov function methods. Subsequently, several key lemmas are established to eliminate Zeno behavior. Then, a preset-time gain function is designed, and a novel proof methodology for preset-time synchronization is proposed to achieve PTS for MNNs. This method involves designing a perturbed dynamical system and reconstructing the controller through time transformation. Compared with existing PTS approaches, the proposed time-scale transformation-based controller design method not only simplifies the synchronization process with improved effectiveness, but also maintains generality. Finally, numerical simulations validate the effectiveness of the theoretical results.