Projective Synchroniztion of Neural Networks via Continuous/Periodic Event-Based Sampling Algorithms

Wang, S; Cao, Y; Wen, S; Guo, Z; Huang, T; Chen, Y

Projective Synchroniztion of Neural Networks via Continuous/Periodic Event-Based Sampling Algorithms

Wang, S Cao, Y Wen, S

Guo, Z Huang, T Chen, Y

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Network Science and Engineering, 2020, 7, (4), pp. 2746-2754
Issue Date:: 2020-10-01

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Field	Value	Language
dc.contributor.author	Wang, S
dc.contributor.author	Cao, Y
dc.contributor.author	Wen, S https://orcid.org/0000-0002-5048-0319
dc.contributor.author	Guo, Z
dc.contributor.author	Huang, T
dc.contributor.author	Chen, Y
dc.date.accessioned	2021-04-28T05:10:59Z
dc.date.available	2021-04-28T05:10:59Z
dc.date.issued	2020-10-01
dc.identifier.citation	IEEE Transactions on Network Science and Engineering, 2020, 7, (4), pp. 2746-2754
dc.identifier.issn	2327-4697
dc.identifier.issn	2327-4697
dc.identifier.uri	http://hdl.handle.net/10453/148455
dc.description.abstract	This study concerns the projective synchronization problem of basic neural networks via continuous/periodic event-based sampling algorithms. Firstly, an event-Triggering control scheme is proposed via continuous sampling. In addition, there exists a consistent positive lower bound for the time interval between two successive trigger events, which implies that the Zeno phenomenon will not occur. Next, by designing an appropriate sampling period, a more practical event-Triggering scheme is proposed with periodic sampling, which can ensure the projective synchronization of the drive-response neural networks systems. Finally, several examples are elaborated to substantiate the theoretical results.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	National Natural Science Foundation of China61673187
dc.relation.ispartof	IEEE Transactions on Network Science and Engineering
dc.relation.isbasedon	10.1109/TNSE.2020.2985409
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Projective Synchroniztion of Neural Networks via Continuous/Periodic Event-Based Sampling Algorithms
dc.type	Journal Article
utslib.citation.volume	7
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	*
pubs.consider-herdc	false
dc.date.updated	2021-04-28T05:10:58Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	4

Abstract:

This study concerns the projective synchronization problem of basic neural networks via continuous/periodic event-based sampling algorithms. Firstly, an event-Triggering control scheme is proposed via continuous sampling. In addition, there exists a consistent positive lower bound for the time interval between two successive trigger events, which implies that the Zeno phenomenon will not occur. Next, by designing an appropriate sampling period, a more practical event-Triggering scheme is proposed with periodic sampling, which can ensure the projective synchronization of the drive-response neural networks systems. Finally, several examples are elaborated to substantiate the theoretical results.

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http://hdl.handle.net/10453/148455