Comparing generic and vectorial nonlinear manoeuvring models and parameter estimation using optimal truncated least square support vector machine. (April 2020)

• Record Type: Journal Article
• Authors: Xu, Haitong; Hassani, Vahid; Guedes Soares, C.
• Is Part Of: Applied ocean research. Volume 97(2020)
• Publication Date: 2020-04
• Subjects: Optimal truncated LS-SVM -- System identification -- Parameter uncertainty -- Nonlinear manoeuvring model -- Generalization performance; Ocean engineering -- Periodicals; 620.416205
• DOI: 10.1016/j.apor.2020.102061
• Languages: English
• ISSNs: 0141-1187

Abstract

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 R 2 goodness-of-fit criterion is used to demonstrate the accuracy of the obtained models.