Sum-based event-triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks

This paper concerns with the event-based dynamic output feedback control for the synchronization of fuzzy neural networks under mixed delay and deception attacks. A weighted sum-based dynamic event-triggered mechanism (WSDETM) is developed to save the communication resources while preserving a satis...

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Bibliographic Details
Published inNeural computing & applications Vol. 35; no. 14; pp. 10221 - 10237
Main Authors Zhang, Duo, Ouyang, Deqiang, Shu, Lan, Hu, Cheng, Shi, Kaibo, Wen, Shiping
Format Journal Article
LanguageEnglish
Published London Springer London 01.05.2023
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Artificial neural networks
Communication
Communications traffic
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Control systems design
Data communication
Data Mining and Knowledge Discovery
Deception
Feedback control
Fuzzy control
Fuzzy logic
Fuzzy sets
Image Processing and Computer Vision
Markov analysis
Markov processes
Neural networks
Original Article
Output feedback
Probability and Statistics in Computer Science
Sensors
Synchronism
Dynamic output feedback
Weighted sum-based dynamic event-triggered scheme
Cone-complimentarity linearization algorithm
T-S fuzzy model
Deception attacks
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Summary:This paper concerns with the event-based dynamic output feedback control for the synchronization of fuzzy neural networks under mixed delay and deception attacks. A weighted sum-based dynamic event-triggered mechanism (WSDETM) is developed to save the communication resources while preserving a satisfactory system performance. A dynamic output feedback controller (DOFC) is designed to achieve exponential synchronization of fuzzy neural networks. To reduce the data traffic, both communication channels from the sensor to DOFC and DOFC to Zero-Order Holder are subject to WSDETM. Different from the traditional deception attacks modeled by Bernoulli process, we adopt the more general Markov process modeling deception attacks. By using the cone-complimentarity linearization algorithm, the DOFC and WSDETM parameters are carried out. The effectiveness of the proposed method is demonstrated with two numerical cases.
Bibliography:ObjectType-Article-1
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content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08231-7