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Non-parametric dynamic system identification of ships using multi-output Gaussian Processes


Ariza Ramirez, W and Leong, ZQ and Nguyen, H and Jayasinghe, SG, Non-parametric dynamic system identification of ships using multi-output Gaussian Processes, Ocean Engineering, 166 pp. 26-36. ISSN 0029-8018 (2018) [Refereed Article]

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2018 Elsevier Ltd. All rights reserved

DOI: doi:10.1016/j.oceaneng.2018.07.056


A novel application of non-parametric system identification algorithm for a surface ship has been employ on this study with the aim of modelling ships dynamics with low quantity of data. The algorithm is based on multi-output Gaussian processes and its ability to model the dynamic system of a ship without losing the relationships between coupled outputs is explored. Data obtained from the simulation of a parametric model of a container ship is used for the training and validation of the multi-output Gaussian processes. The required methodology and metric to implement Gaussian processes for a 4 degrees of freedom (DoF) ship is also presented in this paper. Results show that multi-output Gaussian processes can be accurately applied for non-parametric dynamic system identification in ships with highly coupled DoF.

Item Details

Item Type:Refereed Article
Keywords:dependent Gaussian processes, dynamic system identification, multi-output Gaussian processes, non-parametric identification, oceanic vehicles
Research Division:Mathematical Sciences
Research Group:Applied mathematics
Research Field:Calculus of variations, mathematical aspects of systems theory and control theory
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Ariza Ramirez, W (Mr Wilmer Ariza Ramirez)
UTAS Author:Leong, ZQ (Dr Zhi Leong)
UTAS Author:Nguyen, H (Dr Hung Nguyen)
UTAS Author:Jayasinghe, SG (Dr Shantha Jayasinghe Arachchillage)
ID Code:128647
Year Published:2018
Web of Science® Times Cited:21
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2018-10-04
Last Modified:2019-03-12
Downloads:35 View Download Statistics

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