Asymptotic anti-synchronization of memristor-based BAM neural networks with probabilistic mixed time-varying delays and its application

This paper is concerned with the asymptotic anti-synchronization problem of the memristor-based bidirectional associative memory neural networks (MBAMNNs) and its application in network secure communication. First, we propose a new model of MBAMNNs with probabilistic delays. By establishing a Bernoulli distributed stochastic variable, the information of transmittal time-varying delays is studied. Second, in order to provide a more robust and secure system, we develop a new anti-synchronization model based on the MBAMNNs. The adaptive laws are carefully designed to confirm the process of encryption and decryption in networks secure communication system. Finally, several numerical examples are presented to demonstrate the effectiveness and applicability of our proposed mechanism.