Adaptive Synchronization of Delayed Memristive Neural Networks With Unknown Parameters

Abstract

In this paper, the drive-response synchronization of the delayed memristive neural networks (MNNs) with unknown parameters is studied. With the realization of practical memristors, more and more researchers start to investigate MNNs, and their synchronization problem has became a hot topic. However, the majority of the existing works are based on the strict condition that the weights of MNNs are known and determined. When the parameters are unknown, the obtained results may be inapplicable. Thus, it is worthwhile to investigate the synchronization problem of the delayed MNNs with unknown parameters. Due to the parameter uncertainties of MNNs, a novel response system and an adaptive control method are proposed under different assumptions. The update laws for weights in the response system and the gains of adaptive controllers are developed to synchronize the proposed response system with the delayed MNNs. Furthermore, the proposed methods can be applied to various cases and the corresponding stability theories are established. Finally, the numerical simulations are conducted to verify the effectiveness of the developed methods.