Global exponential anti-synchronization for delayed memristive neural networks via event-triggering method

摘要

This paper studies the exponential anti-synchronization problem of memristive delayed neural networks under the event-triggered controller. To reduce the recalculation of the control signals, two event-triggered control strategies including static and dynamic are proposed. A novel Lyapunov function is constructed to analyze the global exponential anti-synchronization problem. By analysis, we can choose the suitable parameter of the controller to realize global exponential anti-synchronization with a given convergence rate gamma without wasting a lot of control resources. Moreover, under event-triggering conditions given in our theorem, we derive that the Zeno behavior will not happen. Finally, numerical examples are given to validate our theorem.