Quasi-Synchronization of Generalized Inertial Neural Networks Based on Adaptive Event-Triggered Control

In this paper, an adaptive event-triggered control method based on spatiotemporal sampling is proposed for achieving quasi-synchronization of generalized inertial neural networks (GINNs) with reaction—diffusion terms. Compared with the traditional time-sampling event-triggered mechanism, the mechanism proposed in this paper combines both temporal and spatial state information, which not only effectively reduces the event-triggered frequency, but also effectively saves more communication resources. In addition, the quasi-synchronization criterion of GINN is derived by constructing a suitable Lyapunov-Krasovskii function, which ensures the boundedness of the system. Next, by solving a set of linear matrix inequalities, the desired adaptive gain can be obtained. Finally, the effectiveness of the proposed adaptive controller is demonstrated by a numerical simulation.