Event-Triggered Output Feedback Synchronization of Master-Slave Neural Networks Under Deception Attacks
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 p...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 33; no. 3; pp. 952 - 961 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Kazemy, Ali Lam, James Zhang, Xian-Ming |
| Author_xml | – sequence: 1 givenname: Ali orcidid: 0000-0001-7472-224X surname: Kazemy fullname: Kazemy, Ali email: kazemy@tafreshu.ac.ir organization: Department of Electrical Engineering, Tafresh University, Tafresh, Iran – sequence: 2 givenname: James orcidid: 0000-0002-0294-0640 surname: Lam fullname: Lam, James organization: Department of Mechanical Engineering, The University of Hong Kong, Hong Kong – sequence: 3 givenname: Xian-Ming orcidid: 0000-0003-0691-5386 surname: Zhang fullname: Zhang, Xian-Ming organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33108299$$D View this record in MEDLINE/PubMed |
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| Snippet | The problem of event-triggered synchronization of master-slave neural networks is investigated in this article. It is assumed that both communication channels... The problem of event-triggered synchronization of master–slave neural networks is investigated in this article. It is assumed that both communication channels... |
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| SubjectTerms | 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 |
| Title | Event-Triggered Output Feedback Synchronization of Master-Slave Neural Networks Under Deception Attacks |
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