bibtype J - Journal Article
ARLID 0472081
utime 20240103213735.9
mtime 20170306235959.9
SCOPUS 85027532957
WOS 000409048800007
DOI 10.1002/acs.2756
title (primary) (eng) Recursive Bayesian estimation of autoregressive model with uniform noise using approximation by parallelotopes
specification
page_count 9 s.
media_type P
serial
ARLID cav_un_epca*0256772
ISSN 0890-6327
title International Journal of Adaptive Control and Signal Processing
volume_id 31
volume 8 (2017)
page_num 1184-1192
publisher
name Wiley
keyword approximate parameter estimation
keyword ARX model
keyword Bayesian estimation
keyword bounded noise
keyword Kullback-Leibler divergence
keyword parallelotope
author (primary)
ARLID cav_un_auth*0101175
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
full_dept Department of Adaptive Systems
name1 Pavelková
name2 Lenka
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101119
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
full_dept Department of Adaptive Systems
name1 Jirsa
name2 Ladislav
institution UTIA-B
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source
url http://library.utia.cas.cz/separaty/2017/AS/pavelkova-0472081.pdf
cas_special
project
ARLID cav_un_auth*0291242
project_id 7D12004
agency GA MŠk
abstract (eng) This paper proposes a recursive algorithm for the estimation of a stochastic autoregressive model with an external input. The noise of the involved model is described by a uniform distribution. The model parameters are estimated using the Bayesian approach. Without an approximation, the support of the posterior distribution is a complex multidimensional polytope whose number of faces increases with time. We propose an approximation of this polytope in each time step by a parallelotope with a constant number of faces. The behaviour of the proposed algorithm is illustrated by simulations and compared with other methods.
RIV BC
FORD0 10000
FORD1 10200
FORD2 10201
reportyear 2018
num_of_auth 2
mrcbC52 4 A hod 4ah 20231122142310.7
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0270811
mrcbC64 1 Department of Adaptive Systems UTIA-B 20205 AUTOMATION & CONTROL SYSTEMS
confidential S
mrcbC86 3+4 Article Automation Control Systems|Engineering Electrical Electronic
mrcbC86 3+4 Article Automation Control Systems|Engineering Electrical Electronic
mrcbC86 3+4 Article Automation Control Systems|Engineering Electrical Electronic
mrcbT16-e AUTOMATIONCONTROLSYSTEMS|ENGINEERINGELECTRICALELECTRONIC
mrcbT16-j 0.638
mrcbT16-s 0.915
mrcbT16-B 49.11
mrcbT16-D Q3
mrcbT16-E Q2
arlyear 2017
mrcbTft \nSoubory v repozitáři: pavelkova-0472081.pdf
mrcbU14 85027532957 SCOPUS
mrcbU24 PUBMED
mrcbU34 000409048800007 WOS
mrcbU63 cav_un_epca*0256772 International Journal of Adaptive Control and Signal Processing 0890-6327 1099-1115 Roč. 31 č. 8 2017 1184 1192 Wiley