Artículo
Prediction regions based on dissimilarity functions
Autor/es | Carnerero Panduro, Alfonso Daniel
Rodríguez Ramírez, Daniel Lucia, Sergio Alamo, Teodoro |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2023-08 |
Fecha de depósito | 2024-04-23 |
Publicado en |
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Resumen | This paper presents a new methodology to obtain prediction regions of the output of a dynamical system. The proposed approach uses stored past outputs of the system and it is entirely data-based. Only two hyperparameters ... This paper presents a new methodology to obtain prediction regions of the output of a dynamical system. The proposed approach uses stored past outputs of the system and it is entirely data-based. Only two hyperparameters are necessary to apply the proposed methodology. These scalars are chosen so that the size of the obtained regions is minimized while fulfilling the desired empirical probability in a validation set. In this paper, methods to optimally estimate both hyperparameters are provided. The provided prediction regions are convex and checking if a given point belongs to a computed prediction region amounts to solving a convex optimization problem. Also, approximation methods to build ellipsoidal prediction regions are provided. These approximations are useful when explicit descriptions of the regions are necessary. Finally, some numerical examples and comparisons for the case of a non-linear uncertain kite system are provided to prove the effectiveness of the proposed methodology. |
Agencias financiadoras | Agencia Estatal de Investigación. España |
Identificador del proyecto | PID2019-106212RB-C41/AEI/10.1 3039/501100011033 |
Cita | Carnerero, A.D., Rodríguez, D., Lucia, S. y Alamo, T. (2023). Prediction regions based on dissimilarity functions. ISA Transactions, 139, 49-59. https://doi.org/10.1016/j.isatra.2023.03.048. |
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