Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81668
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dc.contributorDepartment of Mechanical Engineering-
dc.creatorNing, HW-
dc.creatorZhang, JM-
dc.creatorJing, X-
dc.creatorTian, TH-
dc.date.accessioned2020-02-10T12:28:31Z-
dc.date.available2020-02-10T12:28:31Z-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10397/81668-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0en_US
dc.rightsThe following publication H. Ning, J. Zhang, X. Jing and T. Tian, "Robust Online Learning Method Based on Dynamical Linear Quadratic Regulator," in IEEE Access, vol. 7, pp. 117780-117795, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2936537en_US
dc.subjectOnline machine learningen_US
dc.subjectOptimal controlen_US
dc.subjectLinear quadratic regulatoren_US
dc.subjectComplex noise disturbancesen_US
dc.titleRobust online learning method based on dynamical linear quadratic regulatoren_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage117780-
dc.identifier.epage117795-
dc.identifier.volume7-
dc.identifier.doi10.1109/ACCESS.2019.2936537-
dcterms.abstractIn this paper, a novel algorithm is proposed for inferring online learning tasks efficiently. By a carefully designed scheme, the online learning problem is first formulated as a state feedback control problem for a series of finite-dimensional systems. Then, the online linear quadratic regulator (OLQR) learning algorithm is developed to obtain the optimal parameter updating. Solid mathematical analysis on the convergence and rationality of our method is also provided. Compared with the conventional learning methods, our learning framework represents a completely different approach with optimal control techniques, but does not introduce any assumption on the characteristics of noise or learning rate. The proposed method not only guarantees the fast and robust convergence but also achieves better performance in learning efficiency and accuracy, especially for the data streams with complex noise disturbances. In addition, under the proposed framework, new robust algorithms can be potentially developed for various machine learning tasks by using the powerful optimal control techniques. Numerical results on benchmark datasets and practical applications confirm the advantages of our new method.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 2019, v. 7, p. 117780-117795-
dcterms.isPartOfIEEE access-
dcterms.issued2019-
dc.identifier.isiWOS:000484314600001-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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