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

Zhang, D; Ouyang, D; Shu, L; Hu, C; Shi, K; Wen, S

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

Zhang, D Ouyang, D Shu, L Hu, C Shi, K Wen, S

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Publisher:: SPRINGER LONDON LTD
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2023, 35, (14), pp. 10221-10237
Issue Date:: 2023-05-01

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Field	Value	Language
dc.contributor.author	Zhang, D
dc.contributor.author	Ouyang, D
dc.contributor.author	Shu, L
dc.contributor.author	Hu, C
dc.contributor.author	Shi, K
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2024-01-16T21:29:08Z
dc.date.available	2024-01-16T21:29:08Z
dc.date.issued	2023-05-01
dc.identifier.citation	Neural Computing and Applications, 2023, 35, (14), pp. 10221-10237
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/174647
dc.description.abstract	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.
dc.language	English
dc.publisher	SPRINGER LONDON LTD
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-023-08231-7
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4611 Machine learning
dc.title	Sum-based event-triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks
dc.type	Journal Article
utslib.citation.volume	35
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-16T21:29:07Z
pubs.issue	14
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	14

Abstract:

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.

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http://hdl.handle.net/10453/174647