Synchronization Control for T-S Fuzzy Neural Networks With Time Delay: A Novel Event-Triggered Mechanism

Gong, S; Guo, Z; Ou, S; Wen, S; Huang, T

Synchronization Control for T-S Fuzzy Neural Networks With Time Delay: A Novel Event-Triggered Mechanism

Gong, S Guo, Z Ou, S Wen, S

Huang, T

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2023, PP, (99), pp. 1-9
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Gong, S
dc.contributor.author	Guo, Z
dc.contributor.author	Ou, S
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Huang, T
dc.date.accessioned	2024-01-16T21:34:00Z
dc.date.available	2024-01-16T21:34:00Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2023, PP, (99), pp. 1-9
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/174652
dc.description.abstract	A novel aperiodic event-triggered control is adopted to address the synchronization issue of T-S fuzzy neural networks (FNNs) with time delay. This control strategy refers to the execution of control tasks in a control system based on real-time events, rather than following a fixed time interval. It allows for more flexible and faster responses to real-time events, and can reduce the computational load, energy consumption, and system costs. At first, a linear event-triggered control mechanism is formulated, in which its triggering condition includes an exponential term. Subsequently, LMI-based synchronization criteria are deduced under the formulated event-triggered control. Additionally, a novel approach that employs the reduction to absurdity technique is proposed to address the non-existence of Zeno behavior. Eventually, the proposed theory's efficacy is demonstrated by employing an example and an accompanying simulation.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2023.3303224
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4901 Applied mathematics
dc.title	Synchronization Control for T-S Fuzzy Neural Networks With Time Delay: A Novel Event-Triggered Mechanism
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
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	2024-01-16T21:33:59Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

A novel aperiodic event-triggered control is adopted to address the synchronization issue of T-S fuzzy neural networks (FNNs) with time delay. This control strategy refers to the execution of control tasks in a control system based on real-time events, rather than following a fixed time interval. It allows for more flexible and faster responses to real-time events, and can reduce the computational load, energy consumption, and system costs. At first, a linear event-triggered control mechanism is formulated, in which its triggering condition includes an exponential term. Subsequently, LMI-based synchronization criteria are deduced under the formulated event-triggered control. Additionally, a novel approach that employs the reduction to absurdity technique is proposed to address the non-existence of Zeno behavior. Eventually, the proposed theory's efficacy is demonstrated by employing an example and an accompanying simulation.

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http://hdl.handle.net/10453/174652