In this paper, adaptive synchronization of memristor-based neural networks (MNNs) with time-varying delays is investigated. The dynamical analysis here employs results from the theory of differential equations with discontinuous right-hand sides as introduced by Filippov. Sufficient conditions for the global synchronization of MNNs are established with a general adaptive controller. The update gain of the controller can be adjusted to control the synchronization speed. The obtained results complement and improve the previously known results. Finally, numerical simulations are carried out to demonstrate the effectiveness of the obtained results.

This paper is concerned with the global exponential stabilization of memristor-based chaotic neural networks with both time-varying delays and general activation functions. Here, we adopt nonsmooth analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new sufficient conditions ensuring exponential stabilization of memristor-based chaotic neural networks are obtained via periodically intermittent control. In addition, the proposed results here are easy to verify and they also extend the earlier publications. Finally, numerical simulations illustrate the effectiveness of the obtained results.

In this brief, we consider the exponential synchronization of chaotic memristive neural networks with time-varying delays using the Lyapunov functional method and inequality technique. The dynamic analysis here employs the theory of differential equations with discontinuous right-hand side as introduced by Filippov. The designing laws in the synchronization of neural networks are proposed via state or output coupling. In addition, the new proposed algebraic criteria are very easy to verify, and they also enrich and improve the earlier publications. Finally, an example is given to show the effectiveness of the obtained results.