Event-triggered synchronization for discrete-time delayed neural networks via aperiodic detection

Abstract

The focus of this paper is to analyze the event-triggered synchronization problem for discrete-time neural networks with time-varying delays. First, an economic event generator is constructed, where the signals are generated by aperiodic detection. The transmission of signals is determined by an aperiodic event-triggered mechanism. This method avoids the continuous sampling and computing for sensors, which results in the saving of computing and communication resources. Second, by taking a piecewise Lyapunov functional, some sufficient conditions are established to ensure the synchronization of the discrete-time neural networks. Compared with the existing results, the proposed Lyapunov functional takes the sawtooth constraint of sampling error signals into account. Third, the control gain and triggering parameter are co-designed by the singular value decomposition technique. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed method.