Modelling of a surface marine vehicle with kernel ridge regression confidence machine
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,...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/13247 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/13247 |
| Access Level: | acceso abierto |
| Palabra clave: | 004.8 System-identification Ship System identification Marine systems Kernel ridge regression (KRR) Conformal predictors (CP) Kernel ridge regression confidence machine (KRRCM) Inteligencia artificial (Informática) 1203.04 Inteligencia Artificial |
| Sumario: | 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. |
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