Finite-time synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

This paper concerns the problem of global and local finite-time synchronization for a class of memristor-based Cohen–Grossberg neural networks with time-varying delays by designing an appropriate feedback controller. Through a nonlinear transformation, we derive an alternative system from the considered memristor-based Cohen–Grossberg neural networks. Then, by considering the finite-time synchronization of the alternative system, we obtain some novel and effective finite-time synchronization criteria for the considered memristor-based Cohen–Grossberg neural networks. These results generalize and extend some previous known works on conventional Cohen–Grossberg neural networks. Finally, numerical simulations are given to present the effectiveness of the theoretical results.