Finite-time synchronization of memristive Cohen-Grossberg neural networks with time delays

This paper discusses the finite-time synchronization of memristor-based Cohen-Grossberg neural networks with time-varying delays. By using nonlinear transformation, an equivalent system is obtained from the discussed neural network. By investigating the finite-time synchronization of the alternative system, some finite-time synchronization criteria of the considered memristor-based Cohen-Grossberg neural network can be obtained. In the whole process, the error state vertical bar e(t)vertical bar are divided into two procedures: vertical bar e(t)vertical bar moving from initial value to 1, next, vertical bar e(t)vertical bar from 1 to 0. Especially, the error state variables e(t) move to 1 in a finite time, and move to 0 in a fixed time. Compared to the classical Lyapunov asymptotic convergence, the finite-time convergence of this paper is a new way to study the synchronization problems. Finally, numerical simulations are presented to display the obtained theoretical results. (c) 2019 Elsevier B.V. All rights reserved.