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Data-driven Modeling of Ship Motion Prediction Based on Support Vector Regression

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

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp17138350

Ingår i: Proceedings of the 58th Conference on Simulation and Modelling (SIMS 58) Reykjavik, Iceland, September 25th – 27th, 2017

Linköping Electronic Conference Proceedings 138:46, s. 350-354

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Publicerad: 2017-09-27

ISBN: 978-91-7685-417-4

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

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.

Nyckelord

Ship Motion time series Prediction, Support Vector Regression

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