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Statements

Subject Item
n2:RIV%2F49777513%3A23520%2F14%3A43923270%21RIV15-GA0-23520___
rdf:type
skos:Concept n20:Vysledek
rdfs:seeAlso
http://dx.doi.org/10.1088/1742-6596/570/1/012002
dcterms:description
This paper is concerned with the problem of recursive system identification using nonparametric Gaussian process model. Non-linear stochastic system in consideration is affine in control and given in the input-output form. The use of recursive Gaussian process algorithm for non-linear system identification is proposed to alleviate the computational burden of full Gaussian process. The problem of an online hyper-parameter estimation is handled using proposed ad-hoc procedure. The approach to system identification using recursive Gaussian process is compared with full Gaussian process in terms of model error and uncertainty as well as computational demands. Using Monte Carlo simulations it is shown, that the use of recursive Gaussian process with an ad-hoc learning procedure offers converging estimates of hyper-parameters and constant computational demands. This paper is concerned with the problem of recursive system identification using nonparametric Gaussian process model. Non-linear stochastic system in consideration is affine in control and given in the input-output form. The use of recursive Gaussian process algorithm for non-linear system identification is proposed to alleviate the computational burden of full Gaussian process. The problem of an online hyper-parameter estimation is handled using proposed ad-hoc procedure. The approach to system identification using recursive Gaussian process is compared with full Gaussian process in terms of model error and uncertainty as well as computational demands. Using Monte Carlo simulations it is shown, that the use of recursive Gaussian process with an ad-hoc learning procedure offers converging estimates of hyper-parameters and constant computational demands.
dcterms:title
Gaussian process based recursive system identification Gaussian process based recursive system identification
skos:prefLabel
Gaussian process based recursive system identification Gaussian process based recursive system identification
skos:notation
RIV/49777513:23520/14:43923270!RIV15-GA0-23520___
n3:aktivita
n19:S n19:P
n3:aktivity
P(ED1.1.00/02.0090), P(GC13-07058J), S
n3:dodaniDat
n8:2015
n3:domaciTvurceVysledku
n7:4200217 n7:5446937
n3:druhVysledku
n16:D
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
17960
n3:idVysledku
RIV/49777513:23520/14:43923270
n3:jazykVysledku
n17:eng
n3:klicovaSlova
Stochastic systems; Gaussian Process; System Identification; Nonliner Systems
n3:klicoveSlovo
n4:Nonliner%20Systems n4:Stochastic%20systems n4:System%20Identification n4:Gaussian%20Process
n3:kontrolniKodProRIV
[85600BC82752]
n3:mistoKonaniAkce
Berlin, Germany
n3:mistoVydani
Bristol
n3:nazevZdroje
Journal of Physics: Conference Series
n3:obor
n14:BC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n10:GC13-07058J n10:ED1.1.00%2F02.0090
n3:rokUplatneniVysledku
n8:2014
n3:tvurceVysledku
Šimandl, Miroslav Prüher, Jakub
n3:typAkce
n21:WRD
n3:zahajeniAkce
2014-11-13+01:00
s:issn
1742-6588
s:numberOfPages
9
n22:doi
10.1088/1742-6596/570/1/012002
n18:hasPublisher
IOP Publishing
n12:organizacniJednotka
23520