New results on exponential synchronization of memristor-based chaotic neural networks

• We establish several sufficient conditions for exponential synchronization.
• We give the estimation of the exponential synchronization convergence rate.
• We obtain the sufficient condition in term of linear matrix inequalities.

This paper investigates the exponential synchronization of a general class of memristor-based recurrent neural networks with variable delay. Then, by using stability theory of Lyapunov functionals and linear matrix inequalities, the simple feedback controller is designed to achieve synchronization between the master neural network and slave neural network and the exponential convergence rate is given by the algebraic equation. The new sufficient condition for the synchronization controller is given in term of linear matrix inequalities (LMI). Two examples are given to verify our results.