Complete periodic adaptive antisynchronization of memristor-based neural networks with mixed time-varying delays

This paper is concerned with the complete periodic antisynchronization issue of memristor-based neural networks with mixed time-varying delays. Under the framework of Filippov solutions of the differential equations with discontinuous right-hand side, based on Mawhin-like coincidence theorem in set-valued analysis theory, the proof of the existence of periodic solution is presented. By applying the Lyapunov-Krasovskii functional approach, adaptive controller is designed and unknown control parameters of the slave system are determined by adaptive laws, and the complete periodic adaptive antisynchronization condition is addressed to ensure the slave system global antisynchronization with the master system. An illustrative example is given to demonstrate the effectiveness of the obtained results.