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dc.contributor.authorWang, Tongtong
dc.contributor.authorLi, Guoyuan
dc.contributor.authorWu, Baiheng
dc.contributor.authorÆsøy, Vilmar
dc.contributor.authorZhang, Houxiang
dc.date.accessioned2021-05-25T08:19:49Z
dc.date.available2021-05-25T08:19:49Z
dc.date.created2021-05-19T14:04:33Z
dc.date.issued2021
dc.identifier.issn1744-5302
dc.identifier.urihttps://hdl.handle.net/11250/2756184
dc.description.abstract.Demanding marine operations increase the complexity of manoeuvring. A highly accurate ship model promotes predicting ship motions and advancing control safety. It is crucial to identify the unknown hydrodynamic coefficients under environmental disturbance to establish accurate mathematical models. In this paper, the identification procedure for a 3 degree of freedom hydrodynamic model under disturbance is completed based on the support vector machine with multiple manoeuvres datasets. The algorithm is validated on the clean ship model and the results present good fitness with the reference. Experiments in different sea states are conducted to investigate the effects of the turbulence on the identification performance. Generalisation results show that the models identified in the gentle and moderate environments have less than 10% deviations and are considered allowable. The higher perturbations, the lower fidelity the identified model has. Models identified under disturbance could provide different levels of reliable support for the operation decision system.en_US
dc.language.isoengen_US
dc.publisherTaylor & Francisen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleParameter Identification of Ship Manoeuvring Model Under Disturbance Using Support Vector Machine Methoden_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.journalShips and Offshore Structuresen_US
dc.identifier.doi10.1080/17445302.2021.1927600
dc.identifier.cristin1910787
dc.description.localcode© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any wayen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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