Quasisynchronization of reaction-diffusion neural networks with time-varying delays by static/dynamic event-triggered control and its application to secure communication

This paper studies the quasisynchronization problems of reaction-diffusion neural networks (RDNNs) with time-varying delays via event-triggered control. Firstly, a static event-triggered mechanism and a dynamic event-triggered mechanism are designed to significantly reduce computation costs and save communication resources, respectively. These two different event-triggered control strategies are also able to meet the requirements of various situations. Based on the static event-triggered mechanism, the dynamic event-triggered mechanism is designed to further reduce the sampling frequency by introducing an internal dynamic variable, and several quasisynchronization criteria are derived. However, the quasisynchronization error bounds are related to triggering parameters and can be flexible adjusted, which reduces the conservatism of the existing quasisynchronization results and extends the application of proposed control strategies. Meanwhile, there exists positive lower bounds for the inter event time which can exclude the Zeno behavior. Finally, numerical simulations are given to demonstrate the superiority of the obtained theoretical results, and one example is given to show the chaotic quasisynchronization of the proposed RDNNs in the application of secure communication.