Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31531
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dc.contributor.authorZhu, K-
dc.contributor.authorWang, Z-
dc.contributor.authorDing, D-
dc.contributor.authorHu, J-
dc.contributor.authorDong, H-
dc.date.accessioned2025-07-10T13:37:10Z-
dc.date.available2025-07-10T13:37:10Z-
dc.date.issued2025-05-09-
dc.identifierORCiD: Kaiqun Zhu https://orcid.org/0000-0002-0658-0806-
dc.identifierORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401-
dc.identifierORCiD: Derui Ding https://orcid.org/0000-0001-7402-6682-
dc.identifierORCiD: Jun Hu https://orcid.org/0000-0002-7852-5064-
dc.identifierORCiD: Hongli Dong https://orcid.org/0000-0001-8531-6757-
dc.identifier.citationZhu, K. et al. (2025) 'Proportional-Integral-Observer-Based Fusion Estimation for Artificial Neural Networks: Implementing a One-Bit Encoding Scheme', IEEE Transactions on Neural Networks and Learning Systems, 0 (early access), pp. 1 - 11. doi: 10.1109/TNNLS.2025.3556370.en_US
dc.identifier.issn2162-237X-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/31531-
dc.description.abstractThis article is concerned with the proportional-integral-observer (PIO)-based fusion estimation problem for a class of artificial neural networks (ANNs) equipped with multiple sensors, which are constrained by bandwidth and subjected to unknown-but-bounded noises (UBBNs). For the purpose of efficient information communication, an approach known as the one-bit encoding mechanism (OBEM) is proposed that enables the encoding of scalar data using merely a single bit. Then, a local PIO-based set-membership estimator is devised for each sensor node, with the aim of achieving the desired estimation task while considering the possible data distortion due to OBEM and the existence of UBBNs. Subsequently, sufficient conditions are established to ensure the existence and effectiveness of the PIO-based set-membership estimator. Moreover, to enhance the global estimation performance, an ellipsoid-based fusion rule is introduced for all local PIO-based set-membership estimators. The performance of fusion estimation is then analyzed using set theory and the optimization method, leading to the determination of relevant parameters. Finally, the effectiveness and advantages of the proposed estimation algorithm are demonstrated through a simulation example.en_US
dc.description.sponsorshipNational Natural Science Foundation of China (Grant Number: 61933007, 62403318, 12471416 and U21A2019); Shanghai Pujiang Program of China (Grant Number: 22PJ1411700); 10.13039/501100000288-Royal Society of U.K.; Alexander von Humboldt Foundation of Germany.en_US
dc.format.extent1 - 11-
dc.format.mediumPrint-Electronic-
dc.languageen-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2025 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 ( https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ ).-
dc.rights.urihttps://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/-
dc.subjectartificial neural networksen_US
dc.subjectset-membership state estimationen_US
dc.subjectfusion estimationen_US
dc.subjectproportional-integral-observeren_US
dc.subjectone-bit encoding mechanism.en_US
dc.titleProportional-Integral-Observer-Based Fusion Estimation for Artificial Neural Networks: Implementing a One-Bit Encoding Schemeen_US
dc.typeArticleen_US
dc.date.dateAccepted2025-03-21-
dc.identifier.doihttps://doi.org/10.1109/TNNLS.2025.3556370-
dc.relation.isPartOfIEEE Transactions on Neural Networks and Learning Systems-
pubs.issue00-
pubs.publication-statusPublished-
pubs.volume0-
dc.identifier.eissn2162-2388-
dcterms.dateAccepted2025-03-21-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Computer Science Research Papers

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