dc.contributor.author Xu, Haitong dc.contributor.author Hassani, Vahid dc.contributor.author Soares, C. Guedes dc.date.accessioned 2020-02-21T12:47:07Z dc.date.accessioned 2020-02-24T14:07:25Z dc.date.available 2020-02-21T12:47:07Z dc.date.available 2020-02-24T14:07:25Z dc.date.issued 2020-01-18 dc.identifier.citation Xu, Hassani, Soares. Comparing generic and vectorial nonlinear manoeuvring models and parameter estimation using optimal truncated least square support vector machine. Applied Ocean Research. 2020 en dc.identifier.issn 0141-1187 dc.identifier.issn 0141-1187 dc.identifier.issn 1879-1549 dc.identifier.uri https://hdl.handle.net/10642/8163 dc.description.abstract An optimal truncated least square support vector machine (LS-SVM) is proposed for the parameter estimation of nonlinear manoeuvring models based on captive manoeuvring tests. Two classical nonlinear manoeuvring models, generic and vectorial models, are briefly introduced, and the prime system of SNAME is chosen as the normalization forms for the hydrodynamic coefficients. The optimal truncated LS-SVM is introduced. It is a robust method for parameter estimation by neglecting the small singular values, which contribute negligibly to the solutions and increase the parameter uncertainty. The parameter with a large uncertainty is sensitive to the noise in the data and have a poor generalization performance. The classical LS-SVM and optimal truncated LS-SVM are used to estimate the parameters, and the effectiveness of optimal truncated LS-SVM is validated. The parameter uncertainty for both nonlinear manoeuvring models is discussed. The generalization performance of the obtained numerical models is further tested against the validation set, which is completely left untouched in the training. The R2 goodness-of-fit criterion is used to demonstrate the accuracy of the obtained models. en dc.description.sponsorship This work was performed within the Strategic Research Plan of the Centre for Marine Technology and Ocean Engineering (CENTEC), which is financed by Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia-FCT) under contract UID/Multi/00134/2013 - LISBOA-01-0145-FEDER-007629. This work was partly supported by the Research Council of Norway through the Centres of Excellence funding scheme, Project number 223254 - AMOS. The PMM data was provided by SINTEF Ocean and were collected in the course of the Knowledge-building Project for the Industry Sea Trials and Model Tests for Validation of Shiphandling Simulation Models'' [59], supported by the Research Council of Norway. en dc.language.iso en en dc.publisher Elsevier en dc.relation.ispartofseries Applied Ocean Research;Volume 97, April 2020 dc.rights © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ en dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/ dc.subject Optimal truncated least square support vector machines en dc.subject System identifications en dc.subject Parameter uncertainties en dc.subject Nonlinear manoeuvring models en dc.subject Generalization performances en dc.title Comparing generic and vectorial nonlinear manoeuvring models and parameter estimation using optimal truncated least square support vector machine en dc.type Journal article en dc.type Peer reviewed en dc.date.updated 2020-02-21T12:47:07Z dc.description.version acceptedVersion en dc.identifier.doi https://dx.doi.org/10.1016/j.apor.2020.102061 dc.identifier.cristin 1796408 dc.source.journal Applied Ocean Research
﻿