Probabilistic interval predictor based on dissimilarity functions

This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the future evolution of the system. The method relies on the use of dissimilarity functions to estimate the conditional prob...

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Detalles Bibliográficos
Autores: Carnerero Panduro, Alfonso Daniel, Rodríguez Ramírez, Daniel, Alamo, Teodoro
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/129839
Acceso en línea:https://hdl.handle.net/11441/129839
https://doi.org/10.1109/TAC.2021.3136137
Access Level:acceso abierto
Palabra clave:Prediction intervals
System identification
Nonlinear systems
Uncertainty
Bounded noise
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spelling Probabilistic interval predictor based on dissimilarity functionsCarnerero Panduro, Alfonso DanielRodríguez Ramírez, DanielAlamo, TeodoroPrediction intervalsSystem identificationNonlinear systemsUncertaintyBounded noiseThis work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the future evolution of the system. The method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs. A family of empirical probability density functions, parameterized by means of two scalars, is introduced. It is shown that the proposed family encompasses the multivariable normal probability density function as a particular case. We show that the presented approach constitutes a generalization of classical estimation methods. A validation scheme is used to tune the two parameters on which the methodology relies. In order to prove the effectiveness of the presented methodology, some numerical examples and comparisons are provided.Institute of Electrical and Electronics Engineers. IEEEIngeniería de Sistemas y AutomáticaTEP950: Estimación, Predicción, Optimización y Control2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/129839https://doi.org/10.1109/TAC.2021.3136137reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Transactions on Automatic Control, Decemberhttps://ieeexplore.ieee.org/document/9653823info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1298392026-06-17T12:51:07Z
dc.title.none.fl_str_mv Probabilistic interval predictor based on dissimilarity functions
title Probabilistic interval predictor based on dissimilarity functions
spellingShingle Probabilistic interval predictor based on dissimilarity functions
Carnerero Panduro, Alfonso Daniel
Prediction intervals
System identification
Nonlinear systems
Uncertainty
Bounded noise
title_short Probabilistic interval predictor based on dissimilarity functions
title_full Probabilistic interval predictor based on dissimilarity functions
title_fullStr Probabilistic interval predictor based on dissimilarity functions
title_full_unstemmed Probabilistic interval predictor based on dissimilarity functions
title_sort Probabilistic interval predictor based on dissimilarity functions
dc.creator.none.fl_str_mv Carnerero Panduro, Alfonso Daniel
Rodríguez Ramírez, Daniel
Alamo, Teodoro
author Carnerero Panduro, Alfonso Daniel
author_facet Carnerero Panduro, Alfonso Daniel
Rodríguez Ramírez, Daniel
Alamo, Teodoro
author_role author
author2 Rodríguez Ramírez, Daniel
Alamo, Teodoro
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
TEP950: Estimación, Predicción, Optimización y Control
dc.subject.none.fl_str_mv Prediction intervals
System identification
Nonlinear systems
Uncertainty
Bounded noise
topic Prediction intervals
System identification
Nonlinear systems
Uncertainty
Bounded noise
description This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the future evolution of the system. The method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs. A family of empirical probability density functions, parameterized by means of two scalars, is introduced. It is shown that the proposed family encompasses the multivariable normal probability density function as a particular case. We show that the presented approach constitutes a generalization of classical estimation methods. A validation scheme is used to tune the two parameters on which the methodology relies. In order to prove the effectiveness of the presented methodology, some numerical examples and comparisons are provided.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/129839
https://doi.org/10.1109/TAC.2021.3136137
url https://hdl.handle.net/11441/129839
https://doi.org/10.1109/TAC.2021.3136137
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Automatic Control, December
https://ieeexplore.ieee.org/document/9653823
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers. IEEE
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers. IEEE
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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