Abstract
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 |e(t)| are divided into two procedures: |e(t)| moving from initial value to 1, next, |e(t)| 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.
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