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  4. Investigation of training data selection in the black-box modeling of ship maneuvering motion
 
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Investigation of training data selection in the black-box modeling of ship maneuvering motion

Citation Link: https://doi.org/10.15480/882.3313
Publikationstyp
Conference Paper
Date Issued
2019-09
Sprache
English
Author(s)
Wang, Zihao  
Soares, Carlos Guedes  
Zou, Zaojian  
Herausgeber*innen
Fluiddynamik und Schiffstheorie M-8  
TORE-DOI
10.15480/882.3313
TORE-URI
http://hdl.handle.net/11420/8968
Article Number
8
Citation
11th International Workshop on Ship and Marine Hydrodynamics (IWSH2019), Paper 8
Contribution to Conference
11th International Workshop on Ship and Marine Hydrodynamics (IWSH2019)  
For the identification modeling of ship maneuvering motion, comparisons between various training data are conducted to select appropriate excitation signal with maximum dynamic information, thereby ensuring the generalization ability of the identified model. The identification framework is black-box modeling based on the (“nu”)-support vector machine algorithm with radial basis function kernel, which automatically controls the number of support vectors and keeps sparsity. A Mariner class ship is taken as the study object, and the training data is generated from the reliable simulation model, including 10º/10º, 20º/20º, 30º/30º zigzag maneuvers and 35º turning circle maneuver. The generalization performance of the identified model under different training data is compared by predicting other standard zigzag and turning maneuvers. The results indicate that the 20º/20º and 30º/30º zigzag maneuvers contain more dynamic information and can be used to train the model when the data is pure. The present work provides guidance for the subsequent experiment research to update the ship model quickly in the field.
Subjects
Identification modelling
Multiple excitation signals
Ship manoeuvring
Support vector machine
DDC Class
600: Technik
620: Ingenieurwissenschaften
More Funding Information
National Natural Science Foundations of China
CSSC Joint Fund Project 2017
China Scholarship Council (CSC)
Lizenz
http://rightsstatements.org/vocab/InC/1.0/
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