Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31527
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dc.contributor.authorZhang, Y-
dc.contributor.authorWang, Z-
dc.contributor.authorZou, L-
dc.contributor.authorQian, W-
dc.contributor.authorDu, S-
dc.date.accessioned2025-07-10T10:29:08Z-
dc.date.available2025-07-10T10:29:08Z-
dc.date.issued2025-03-21-
dc.identifierORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401-
dc.identifierORCiD: Lei Zou https://orcid.org/0000-0002-0409-7941-
dc.identifierORCiD: Wei Qian https://orcid.org/0000-0002-3994-6501-
dc.identifierORCiD: Shuxin Du https://orcid.org/0000-0002-8530-4884-
dc.identifier.citationZhang, Y. et al. (2025) 'Neural-Network-Based Recursive State Estimation for Nonlinear Networked Systems With Binary-Encoding Mechanisms', IEEE Transactions on Neural Networks and Learning Systems, 36 (5), pp. 10072 - 10083. doi: 10.1109/TNNLS.2025.3542492.en_US
dc.identifier.issn2162-237X-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/31527-
dc.description.abstractThis work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.en_US
dc.description.sponsorship10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62273087 and 61933007); 10.13039/501100001809-Royal Society of U.K., and in part by the Alexander von Humboldt Foundation of Germany.en_US
dc.format.extent10072 - 10083-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
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.subjectnetworked nonlinear systemsen_US
dc.subjectneural networksen_US
dc.subjectunknown nonlinearitiesen_US
dc.subjectrecursive state estimationen_US
dc.subjectbinary-encoding mechanismen_US
dc.titleNeural-Network-Based Recursive State Estimation for Nonlinear Networked Systems With Binary-Encoding Mechanismsen_US
dc.typeArticleen_US
dc.date.dateAccepted2025-02-02-
dc.identifier.doihttps://doi.org/10.1109/TNNLS.2025.3542492-
dc.relation.isPartOfIEEE Transactions on Neural Networks and Learning Systems-
pubs.issue5-
pubs.publication-statusPublished-
pubs.volume36-
dc.identifier.eissn2162-2388-
dcterms.dateAccepted2025-02-02-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Computer Science Research Papers

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