General decay projection synchronization of proportional delay memristive competitive neural networks with uncertain parameters for image encryption

Abstract

This research delves into the general decay projection synchronization (GDPS) in memristive competitive neural networks (MCNNs) incorporating proportional delays and uncertain parameters. There are two main aspects to consider: one is to add switch controllers to both long-term memory (LTM) and short-term memory (STM), and the other is that given that MCNNs are coupled systems, we only add the controller to STM. Then, by formulating appropriate Lyapunov functional and using inequality methods, algebraic synchronization criteria for implementing GDPS behavior are derived, all of which rely on proportional delayed factors. To validate the effectiveness of our proposed methods, we conduct two numerical examples and apply one of them to image encryption.