Matrix measure strategies for exponential synchronization and anti-synchronization of memristor-based neural networks with time-varying delays

This paper is concerned with exponential synchronization and anti-synchronization of memristor-based neural networks. Under the framework of Filippov systems and a linear controller, the exponential synchronization and anti-synchronization criteria for memristor-based neural networks can be guaranteed by the matrix measure and Halanay inequality. The criteria are very simple to implement in practice. Finally, two numerical examples are given to demonstrate the correctness of the theoretical results. It is shown that the matrix measure can increase the exponential convergence rate and decrease the feedback gain effectively.