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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/27403
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dc.contributor.authorSong, W-
dc.contributor.authorWang, Z-
dc.contributor.authorLi, Z-
dc.contributor.authorHan, Q-L-
dc.contributor.authorYue, D-
dc.date.accessioned2023-10-17T15:40:31Z-
dc.date.available2023-10-17T15:40:31Z-
dc.date.issued2023-08-21-
dc.identifierORCID iD: Weihao Song https://orcid.org/0000-0003-3604-3224-
dc.identifierORCID iD: Zidong Wang https://orcid.org/0000-0002-9576-7401-
dc.identifierORCID iD: Zhongkui Li https://orcid.org/0000-0002-9361-4305-
dc.identifierORCID iD: Qing-Long Han https://orcid.org/0000-0002-7207-0716-
dc.identifierORCID iD: Dong Yue https://orcid.org/0000-0001-7810-9338-
dc.identifier.citationSong, W. et al. (2023) 'Maximum Correntropy Filtering for Complex Networks With Uncertain Dynamical Bias: Enabling Componentwise Event-Triggered Transmission', IEEE Transactions on Neural Networks and Learning Systems, 2023, 0 (early access), pp. 1 - 14. doi: 10.1109/tnnls.2023.3302190.en_US
dc.identifier.issn2162-237X-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/27403-
dc.description.sponsorship10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62203016, U2241214, T2121002 and 61933007);; 10.13039/501100002858-China Postdoctoral Science Foundation (Grant Number: 2021TQ0009); Royal Society, U (Grant Number: 0000DONOTUSETHIS0000.K); Alexander von Humboldt Foundation of Germany.en_US
dc.format.extent1 - 14-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See: https://www.ieee.org/publications/rights/rights-policies.html-
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html-
dc.subjectcomplex networksen_US
dc.subjectdynamic event-triggered protocolen_US
dc.subjectdynamical biasen_US
dc.subjectmaximum correntropy filtering (MCF)en_US
dc.subjectnon-Gaussian noisesen_US
dc.titleMaximum Correntropy Filtering for Complex Networks With Uncertain Dynamical Bias: Enabling Componentwise Event-Triggered Transmissionen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/tnnls.2023.3302190-
dc.relation.isPartOfIEEE Transactions on Neural Networks and Learning Systems-
pubs.issueearly access-
pubs.publication-statusPublished-
pubs.volume0-
dc.identifier.eissn2162-2388-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
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