Abstract
This paper investigates the adaptive synchronization of memristor-based neural networks (MNNs) with discontinuous activations. First, by using a decoupling strategy, the extensively used models of MNNs are improved, which can describe the real dynamics more accurately. Moreover, we extend the asymptotic synchronization criteria for MNNs with the consideration of discontinuous activation functions. Additionally, a general adaptive controller is devised to synchronize the drive and response systems, and the sufficient stability conditions are established via Lyapunov functional method within the framework of differential inclusions. Finally, numerical simulations are conducted to show the effectiveness of the developed methods.
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