New Results of Finite-Time Synchronization via Piecewise Control for Memristive Cohen-Grossberg Neural Networks With Time-Varying Delays

Abstract

This paper presents the finite-time synchronization (FTS) via piecewise control laws for a class of memristive Cohen-Grossberg neural networks (MCGNNs) with time-varying delays. First, based on memristive neural network theory, differential inclusion theory, and stability theory, several new sufficient conditions are established to ensure the FTS stability of a class of MCGNNs with time-varying delays. Then, three control laws are designed. By comparison with a normal control law, the piecewise control law determined by finite-time control (FTC) θ(t) can shorten the settling time. Also, the piecewise control law determined by the dynamic error ∥e∥(t)II and FTC 8(t) can shorten the settling time. Finally, a numerical simulation example is provided to illustrate the effectiveness of the new methods.