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

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) theta(t) can shorten the settling time. Also, the piecewise control law determined by the dynamic error parallel to epsilon(t)parallel to and FTC theta(t) can shorten the settling time. Finally, a numerical simulation example is provided to illustrate the effectiveness of the new methods.