4.7 Article

Quantized Event-Triggered Synchronization of Discrete-Time Chaotic Neural Networks With Stochastic Deception Attack

Journal

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 53, Issue 7, Pages 4511-4521

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2023.3251355

Keywords

Chaotic communication; Synchronization; Biological neural networks; Quantization (signal); Stochastic processes; Delays; Delay effects; Cyberattack; discrete-time chaotic neural networks; quantized event-triggered synchronization

Ask authors/readers for more resources

This article focuses on the event-triggered synchronization of delayed discrete-time chaotic neural networks with quantized effect and stochastic deception attack. An event-triggered mechanism and a logarithmic quantizer are employed to alleviate network communication and burden. A synchronization error model is introduced to integrate the impact of event-triggered scheme, quantization, and cyberattack. The proposed method is verified by numerical examples.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available