Synchronization of Neural Networks: Prescribed Performance-Based Event-Triggered Impulsive Control

This paper focuses on the synchronization of neural networks (NNs) via prescribed performance-based event-triggered impulsive control (ETIC). By integrating ETIC methods with prescribed performance theory, an innovative event-triggered controller is designed, which incorporates prescribed performance constraints. Through designing a performance function and employing error transformation techniques, the steady-state performance of synchronization errors is ensured, while optimizing the system's transient performance. The proposed control method significantly reduces the data transmission, effectively saves network resources. Utilizing Lyapunov analysis method, establishes sufficient conditions to guarantee the system's synchronization under prescribed performance requirements. Finally, simulation results validate the superior performance of the proposed control strategy,