Event-Triggered Hybrid Impulsive Control on Lag Synchronization of Delayed Memristor-Based Bidirectional Associative Memory Neural Networks for Image Hiding

Abstract

Memristor-based bidirectional associative memory neural networks (MBAMNNs) analysis can help to reveal the dynamic basis of secure communication. Effectively representing the synchronization of MBAMNNs is the principal task of applying brain-inspired neural networks to image hiding. Previous research has typically utilized continuous control methods to represent the synchronization of MBAMNNs, but these are not well coordinated under limited network bandwidth. Besides, MBAMNNs provide special communication features and uncertain factors to describe the image hiding, but few studies have explored such elements. To address these two issues, we propose an event-triggered hybrid impulsive control on lag synchronization of MBAMNNs for the image hiding. Specifically, we incorporate both time-varying uncertain factors and topology structure into a designed event-triggered controller. The frequency of the controller can be automatically updated from two novel triggered conditions, and Zeno-behavior can be avoided effectively. Experimental results demonstrate that our scheme not only outperforms several exciting approaches in synchronization but also can effectively realize the image hiding under low energy consumption.

Keywords: BAM neural networks, Event-triggered control, Image hiding, Lag synchronization, Memristor