Quantized Event-Triggered Synchronization of Discrete-Time Chaotic Neural Networks With Stochastic Deception Attack
Yajuan Liu, Zhao Fang, Ju H. Park, Fang Fang
Abstract
This article focuses on the event-triggered synchronization of delayed discrete-time chaotic neural networks with quantized effect and stochastic deception attack. First, for alleviating the network communication and communication burden, an event-triggered mechanism and a logarithmic quantizer are employed, separately. Second, for integrating the impact of event-triggered scheme, quantization, and cyberattack in a unified framework, a synchronization error model is introduced. Third, based on the Lyapunov–Krasvovskii functional (LKF), some sufficient conditions are established to guarantee the synchronization of drive system and response system. Furthermore, the co-design controller and homologous event-triggered parameters are also derived according to the presented asymptotic stability condition. Finally, the availability of the proposed method is verified by some numerical examples.