Intermittent discrete adaptive event-triggered synchronization of state-dependent switching neural networks and its application.

Abstract

This paper investigates the synchronization control problem for a class of state-dependent switching neural networks with time-varying delay and semi-Markov jump parameters. A new intermittent discrete adaptive event-triggered control scheme is proposed to reduce control cost and communication burden. Specifically, a time-window mechanism is introduced to characterize the intermittent operation, where the activation width is determined by the synchronization error state at the beginning of each intermittent period. Furthermore, the triggering threshold is updated in a discrete adaptive manner according to the variation between the current sampled state and the most recently transmitted state. A switched Lyapunov-Krasovskii functional is constructed by incorporating the characteristics of semi-Markov jumps, time-varying delay, sampling period, and intermittent operation. Based on this framework, sufficient conditions are derived to guarantee exponential synchronization of the considered master-slave system. Finally, two numerical examples and two image encryption-decryption applications are provided to demonstrate the effectiveness of the proposed results.