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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 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, 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:	2
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2018-10-04
Last Modified:	2019-03-12
Downloads:	8 View Download Statistics