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Black-Box Marine Vehicle Identification with Regression Techniques for Random Manoeuvres

Department of Computer Science and Automatic Control, University of Distance Learning Education, UNED Madrid, 28040 Madrid, Spain
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Author to whom correspondence should be addressed.
The authors contributed equally to this work.
Electronics 2019, 8(5), 492; https://doi.org/10.3390/electronics8050492
Received: 8 April 2019 / Revised: 21 April 2019 / Accepted: 27 April 2019 / Published: 30 April 2019
(This article belongs to the Section Electrical and Autonomous Vehicles)
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Abstract

As a critical step to efficiently design control structures, system identification is concerned with building models of dynamical systems from observed input–output data. In this paper, a number of regression techniques are used for black-box marine system identification of a scale ship. Unlike other works that train the models using specific manoeuvres, in this work the data have been collected from several random manoeuvres and trajectories. Therefore, the aim is to develop general and robust mathematical models using real experimental data from random movements. The techniques used in this work are ridge, kernel ridge and symbolic regression, and the results show that machine learning techniques are robust approaches to model surface marine vehicles, even providing interpretable results in closed form equations using techniques such as symbolic regression. View Full-Text
Keywords: Marine identification; ridge regression; symbolic regression; modelling Marine identification; ridge regression; symbolic regression; modelling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Moreno, R.; Moreno-Salinas, D.; Aranda, J. Black-Box Marine Vehicle Identification with Regression Techniques for Random Manoeuvres. Electronics 2019, 8, 492.

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