PublicaciĆ³n: Modelling of a surface marine vehicle with kernel ridge regression confidence machine
Fecha
2018-12-27
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Derechos de acceso
info:eu-repo/semantics/openAccess
TĆtulo de la revista
ISSN de la revista
TĆtulo del volumen
Editor
Elsevier
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
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