Truncated least square support vector machine for parameter estimation of a nonlinear manoeuvring model based on PMM tests
Journal article, Peer reviewed
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A new version of least square support vector machine (LS-SVM), the truncated LS-SVM, is proposed to estimate the nondimensionalized hydrodynamic coefficients. Truncated LS-SVM is shown to be an efficient and robust method that avoids the costly matrix inversion operation in classical LS-SVM using the singular values decomposition. Meanwhile, the smaller singular values are neglected considering their negligible contribution to the solutions. In order to get a robust parameter estimation, the model simplification of the nonlinear manoeuvring model is carried out using a leave-one-out method, considering the trade-off between the parameter uncertainty and accuracy of the numerical model. The simplified manoeuvring model and the values of nondimensionalized hydrodynamic coefficients are presented. The coefficients are estimated using Planar motion mechanism (PMM) tests, which were carried out in a towing tank. The validation process is carried to validate the generalization performance of the obtained numerical model using the PMM test data.