PublicaciĆ³n:
Modelling of a surface marine vehicle with kernel ridge regression confidence machine

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Modelling of a surface marine vehicle with kernel ridge.pdf (5.97 MB)
Fecha
2018-12-27
Autores
Moreno, Raul
Pereira, Augusto
Cruz, Jesus M. de la
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Derechos de acceso
info:eu-repo/semantics/openAccess
TĆ­tulo de la revista
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Editor
Elsevier
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Resumen
This paper describes the use of Kernel Ridge Regression (KRR) and Kernel Ridge Regression Confidence Machine (KRRCM) for black box identification of a surface marine vehicle. Data for training and test have been obtained from several manoeuvres typically used for marine system identification. Thus, a 20/20 degrees Zig-Zag, a 10/10 degrees Zig-Zag, and different evolution circles have been employed for the computation and validation of the model. Results show that the application of conformal prediction provides an accurate model that reproduces with large accuracy the actual behaviour of the ship with confidence margins that ensure that the model response is within these margins, making it a suitable tool for system identification.
DescripciĆ³n
The registered version of this article, first published in Applied Soft Computing, is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.asoc.2018.12.002
La versiĆ³n registrada de este artĆ­culo, publicado por primera vez en Applied Soft Computing, estĆ” disponible en lĆ­nea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.asoc.2018.12.002
CategorĆ­as UNESCO
Palabras clave
system identification, marine systems, Kernel Ridge Regression (KRR), Conformal Predictors (CP), Kernel Ridge Regression Confidence Machine (KRRCM)
CitaciĆ³n
David Moreno-Salinas, Raul Moreno, Augusto Pereira, Joaquin Aranda, Jesus M. de la Cruz, Modelling of a surface marine vehicle with kernel ridge regression confidence machine, Applied Soft Computing, Volume 76, 2019, Pages 237-250
Centro
E.T.S. de Ingenieros Industriales
Departamento
InformƔtica y AutomƔtica
Grupo de investigaciĆ³n
Grupo de innovaciĆ³n
Programa de doctorado
CƔtedra
Handle
https://hdl.handle.net/20.500.14468/26209
DOI
https://doi.org/10.1016/j.asoc.2018.12.002
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