Moreno Salinas, DavidMoreno, RaulPereira, AugustoAranda Almansa, JoaquínCruz, Jesus M. de la2025-03-102025-03-102018-12-27David 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 237-2501872-9681https://doi.org/10.1016/j.asoc.2018.12.002https://hdl.handle.net/20.500.14468/26209The 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.2018.12.002La versión registrada de este artículo, publicado por primera vez en Applied Soft Computing, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.asoc.2018.12.002This 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.eninfo:eu-repo/semantics/openAccess1203 Ciencia de los ordenadoresartículosystem identificationmarine systemsKernel Ridge Regression (KRR)Conformal Predictors (CP)Kernel Ridge Regression Confidence Machine (KRRCM)