Event-Triggered Output Feedback Synchronization of Master-Slave Neural Networks Under Deception Attacks

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 3; pp. 952 - 961
• Main Authors: Kazemy, Ali, Lam, James, Zhang, Xian-Ming
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Channels; Communication; Communication channels; Computer crime; Data communication; Data transmission; Deception; Deception-attacks; event-triggered mechanisms; Feedback; Feedback control; Linear matrix inequalities; Manganese; Markov Chains; Markov processes; Mathematical analysis; Neural networks; Neural Networks, Computer; Output feedback; Stochasticity; Synchronism; Synchronization
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3030638

The problem of event-triggered synchronization of master-slave neural networks is investigated in this article. It is assumed that both communication channels from the sensor to controller and from controller to actuator are subject to stochastic deception attacks modeled by two independent Markov processes. Two discrete event-triggered mechanisms are introduced for both channels to reduce the number of data transmission through the communication channels. To comply with practical point of view, static output feedback is utilized. By employing the Lyapunov-Krasovskii functional method, some sufficient conditions on the synchronization of master-slave neural networks are derived in terms of linear matrix inequalities, which make it easy to design suitable output feedback controllers. Finally, a numerical example is presented to show the effectiveness of the proposed method.