Performance assessment of sequential Bayesian processors based on probably approximately correct computation and information theory
Author
dc.contributor.author
Jaras, I.
Author
dc.contributor.author
Orchard Concha, Marcos
Admission date
dc.date.accessioned
2018-07-17T16:51:01Z
Available date
dc.date.available
2018-07-17T16:51:01Z
Publication date
dc.date.issued
2018
Cita de ítem
dc.identifier.citation
Electronics Letters, 54 (6): 357-358
es_ES
Identifier
dc.identifier.other
10.1049/el.2017.4159
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/149940
Abstract
dc.description.abstract
A novel method to characterise the efficacy and efficiency of different sequential Bayesian processor implementations is proposed. This method is based on concepts of probably approximately correct computation and information theory measures. The proposed approach is used to compare the performance of three different Bayesian estimation algorithms (particle filter, unscented Kalman filter (UKF), and UKF with outer feedback correction loops) in the context of lithium-ion battery state-of-charge monitoring.
es_ES
Patrocinador
dc.description.sponsorship
CONICYT FONDECYT
1170044
CONICYT
PIA ACT 1405
FB0008