Event-Triggered quasi-synchronization of neural networks with hidden Markov model-based asynchronous target

This article is concerned with the event-triggered quasi-synchronization for discrete Markov jump neural networks (MJNNs). Considering that the slave system cannot capture synchronously master system modes in real-world applications, a hidden Markov model is introduced to describe the resultant mode mismatches. To pursue a desired balance between the synchronization performance and the event-triggered transmission, a more general event-triggered protocol is constructed by developing the threshold parameter as a diagonal matrix. Subsequently, the sufficient condition for event-triggered quasi-synchronization of MJNNs is proposed with the assistance of Lyapunov techniques. Moreover, resorting to an iterative algorithm and the linear matrix inequality, the tighter error bound is obtained. Finally, a numerical example demonstrates effectiveness of the control scheme via a comparison of conservatism between the proposed approach and the existing one.

Automated summary

This article focuses on event-triggered quasi-synchronization in discrete Markov jump neural networks (MJNNs). By introducing a hidden Markov model, the mode mismatches in real-world applications are described. A more general event-triggered protocol is constructed by developing the threshold parameter as a diagonal matrix to achieve a desired balance between synchronization performance and event-triggered transmission. The sufficient condition for event-triggered quasi-synchronization of MJNNs is proposed using Lyapunov techniques, and a tighter error bound is obtained through an iterative algorithm and linear matrix inequality. A numerical example is provided to demonstrate the effectiveness of the control scheme by comparing the conservatism between the proposed approach and the existing one.