Global polynomial synchronization of proportional delay memristive neural networks with uncertain parameters and its application to image encryption

Abstract

This article explores the global polynomial synchronization (GPS) for a type of proportional delay memristive neural networks (PDMNNs), uncertain parameters are considered. First, the theory of differential inclusion is utilized, and then the error system is obtained. Secondly, combining the principles of sliding mode control (SMC) and adaptive control, two different controllers are designed to achieve GPS between the obtained drive–response system. Then, two GPS criteria are obtained through the application of Lyapunov stability theory and inequality analysis techniques. Ultimately, we offer three numerical exemplifications to corroborate the efficacy of the obtained results, along with a demonstration of an application pertaining to image encryption.