Synchronization Control of Memristive Multidirectional Associative Memory Neural Networks and Applications in Network Security Communication

In this paper, we investigate the synchronization in the mean square sense of memristive multidirectional associative memory neural networks with mixed time-varying delays and stochastic perturbations. In the proposed approach, the mixed delays include time-varying delays and distributed time delays. Sufficient criteria guaranteeing the synchronization of the drive-response system are derived based on the drive-response concept, the stochastic differential theory and Lyapunov function. With the removal of certain strict conditions of weight parameters, less conservative results are generated. To illustrate the performance of the proposed synchronization criteria, a secure communication scheme to realize secure data transmission is designed. Meanwhile, the effectiveness of the proposed theories is validated with numerical experiments.

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