Catalogus

Zoeken

Zoeken kan via de modus 'eenvoudig zoeken' (één veld) of uitgebreid via 'geavanceerd zoeken' (meerdere velden). Zo kan je bv. zoeken op een combinatie van een auteursnaam (auteur), een jaartal (jaar) en een documenttype.

Boekenmand

Nuttige resultaten kan je aanvinken en toevoegen aan een mandje. De inhoud hiervan kan je exporteren of afdrukken (naar bv. PDF).

RSS

Op de hoogte blijven van nieuw toegevoegde publicaties binnen uw interessegebied? Dit kan door een RSS-feed (?) te maken van jouw zoekopdracht.

log in

Publicaties | Kaarten

nieuwe zoekopdracht

[ meld een fout in dit record ]

mandje (1): toevoegen | toon

---

one publication added to basket [313589]

Parameter estimation for a ship’s roll response model in shallow water using an intelligent machine learning method Chen, C.; Tello Ruiz, M.Á.; Delefortrie, G.; Mei, T.; Lataire, E.; Vantorre, M. (2019). Parameter estimation for a ship’s roll response model in shallow water using an intelligent machine learning method, in: Candries, M. et al. 5th MASHCON International Conference on Ship Manoeuvring in Shallow and Confined Water with non-exclusive focus on manoeuvring in waves, wind and current, 19 - 23 May 2019, Ostend, Belgium. pp. 51-59 In: Candries, M. et al. (2019). 5th MASHCON International Conference on Ship Manoeuvring in Shallow and Confined Water with non-exclusive focus on manoeuvring in waves, wind and current, 19 - 23 May 2019, Ostend, Belgium: conference proceedings. Flanders Hydraulics Research/Ghent University. Maritime Technology Division: Antwerp. XIX, 534 pp., meer

---

Beschikbaar in	Auteurs
Waterbouwkundig Laboratorium: Open Repository 334545 [ OWA ]
Documenttype: Congresbijdrage

---

Author keywords

Roll model; Shallow water; NLS-SVM; Damping parameters; Parameter identification

---

Auteurs		Top
Changyuan, C. Tello Ruiz, M.Á., meer Delefortrie, G., meer	Mei, T. Lataire, E., meer Vantorre, M., meer

---

Abstract

In order to accurately identify the ship’s roll model parameters in shallow water, and solve the problems of difficult estimating nonlinear damping coefficients by traditional methods, a novel Nonlinear Least Squares - Support Vector Machine (NLS-SVM) is introduced. To illustrate the validity and applicability of the proposed method, simulation data and decay tests data are combined and utilized to estimate unknown parameters and predict the roll motions. Firstly, simulation data is applied in the NLS-SVM model to obtain estimated damping parameters, compared with pre-defined parameters to verify the validity of the proposed method. Subsequently, decay tests data are used in identifying unknown parameters by utilizing traditional models and the new NLS-SVM model, the results illustrate that the intelligent method can improve the accuracy of parametric estimation, and overcome the conventional algorithms’ weakness of difficult identification of the nonlinear damping parameter in the roll model. Finally, to show the wide applicability of the proposed model in shallow water, experimental data from various speeds and Under Keel Clearances (UKCs) are applied to identify the damping coefficients. Results reveal the potential of using the NLS-SVM for the problem of the roll motion in shallow water, and the effectiveness and accuracy are verified as well.

---

Alle informatie in het Integrated Marine Information System (IMIS) valt onder het VLIZ Privacy beleid

Top | Auteurs