Master–slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control

Abstract

This paper investigates master–slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master–slave exponential synchronization of the neural networks. Thirdly, the master–slave synchronization problems are transformed into the stability problems of the master–slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov–Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results.