A waiting-time-based event-triggered scheme for stabilization of complex-valued neural networks

Abstract

This paper addresses the global stabilization of complex-valued neural networks (CVNNs) via event-triggered control. First, a waiting-time-based event-triggered scheme is designed to reduce the data transmission rate. Therein, an exponential decay term is introduced into the predefined threshold function, which may postpone the triggering instant of the necessary data and therefore reduce the frequency of data transmission. Then, with the help of the input delay approach, a time-dependent piecewise-defined Lyapunov–Krasovskii functional is constructed for closed-loop system to formulate a less conservative stability criterion. In addition, by resorting to matrix transformation, the co-design method for both the feedback gains and the trigger parameters is derived. Finally, a numerical example is given to illustrate the feasibility and superiority of the proposed event-triggered scheme and the obtained theoretical results.