Pointwise-Measurement-Based Event-Triggered Synchronization of Reaction-Diffusion Neural Networks

In this paper, the synchronization issue of reaction-diffusion neural networks (RDNNs) is addressed based on a dynamic event-triggered mechanism (DETM). First, pointwise measurement is used to balance system design cost and system performance, and DETM is employed to reduce the utilization of communication resources. Subsequently, the pointwise control is performed at certain specific points to save the number of actuators. In addition, the stability criteria for a closed-loop error system with less conservativeness are derived by using a time-dependent Lyapunov-Krasovskii functional and integrating some inequalities. Finally, a numerical example is provided to illustrate the validity of the proposed synchronization scheme.