Article | Proceedings of the 58th Conference on Simulation and Modelling (SIMS 58) Reykjavik, Iceland, September 25th ‚Äď 27th, 2017 | Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression
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Title:
Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression
Author:
Bikram Kawan: Faculty of Engineering and Natural Sciences, Norwegian University of Science and Technology, Norway Hao Wang: Faculty of Engineering and Natural Sciences, Norwegian University of Science and Technology, Norway Guoyuan Li: Faculty of Maritime Technology and Operations, Norwegian University of Science and Technology, Norway Khim Chhantyal: Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway, Norway
DOI:
10.3384/ecp17138350
Download:
Full text (pdf)
Year:
2017
Conference:
Proceedings of the 58th Conference on Simulation and Modelling (SIMS 58) Reykjavik, Iceland, September 25th ‚Äď 27th, 2017
Issue:
138
Article no.:
046
Pages:
350-354
No. of pages:
5
Publication type:
Abstract and Fulltext
Published:
2017-09-27
ISBN:
978-91-7685-417-4
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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This paper presents a flexible system structure to analyze and model for the potential use of huge ship sensor data to generate efficient ship motion prediction model. The noisy raw data is cleaned using noise reduction, resampling and data continuity techniques. For modeling, a flexible Support Vector Regression (SVR) is proposed to solve regression problem. In the data set, sensitivity analysis is performed to find the strength of input attributes for prediction target. The highly significant attributes are considered for input feature which are mapped into higher dimensional feature using non-linear function, thus SVR model for ship motion prediction is achieved. The prediction results for trajectory and pitch show that the proposed system structure is efficient for the prediction of different ship motion attributes.

Keywords: Ship Motion time series Prediction, Support Vector Regression

Proceedings of the 58th Conference on Simulation and Modelling (SIMS 58) Reykjavik, Iceland, September 25th ‚Äď 27th, 2017

Author:
Bikram Kawan, Hao Wang, Guoyuan Li, Khim Chhantyal
Title:
Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression
DOI:
http://dx.doi.org/10.3384/ecp17138350
References:
No references available

Proceedings of the 58th Conference on Simulation and Modelling (SIMS 58) Reykjavik, Iceland, September 25th ‚Äď 27th, 2017

Author:
Bikram Kawan, Hao Wang, Guoyuan Li, Khim Chhantyal
Title:
Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression
DOI:
http://dx.doi.org/10.3384/ecp17138350
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Last updated: 2017-02-21