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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F09%3A00158517%21RIV10-GA0-21230___
rdf:type
skos:Concept n16:Vysledek
dcterms:description
The problem of estimating parameters of linear models from noisy time series measurements of continuous dynamical systems is considered. Additional prior information (grey box) is used to improve the quality of the estimate, which can be useful, when obtaining sufficiently excited input/output experiment data is impracticable or costly. The problem is solved with respect to optimal multistep prediction for better performance in advanced control (model predictive control). This leads to a nonconvex numerical optimization, where the solver can easily run out in one of the many local extremes. Two approaches, how to improve the convergence, are introduced. Finally an example of identification for combustion control, based on real power plant data, is presented. The problem of estimating parameters of linear models from noisy time series measurements of continuous dynamical systems is considered. Additional prior information (grey box) is used to improve the quality of the estimate, which can be useful, when obtaining sufficiently excited input/output experiment data is impracticable or costly. The problem is solved with respect to optimal multistep prediction for better performance in advanced control (model predictive control). This leads to a nonconvex numerical optimization, where the solver can easily run out in one of the many local extremes. Two approaches, how to improve the convergence, are introduced. Finally an example of identification for combustion control, based on real power plant data, is presented. The problem of estimating parameters of linear models from noisy time series measurements of continuous dynamical systems is considered. Additional prior information (grey box) is used to improve the quality of the estimate, which can be useful, when obtaining sufficiently excited input/output experiment data is impracticable or costly. The problem is solved with respect to optimal multistep prediction for better performance in advanced control (model predictive control). This leads to a nonconvex numerical optimization, where the solver can easily run out in one of the many local extremes. Two approaches, how to improve the convergence, are introduced. Finally an example of identification for combustion control, based on real power plant data, is presented.
dcterms:title
Identifikace grey-box modelu s praktickou aplikací v průmyslu a energetice Grey box model identification with practical application to combustion control Identifikace grey-box modelu s praktickou aplikací v průmyslu a energetice
skos:prefLabel
Identifikace grey-box modelu s praktickou aplikací v průmyslu a energetice Identifikace grey-box modelu s praktickou aplikací v průmyslu a energetice Grey box model identification with practical application to combustion control
skos:notation
RIV/68407700:21230/09:00158517!RIV10-GA0-21230___
n3:aktivita
n10:P
n3:aktivity
P(GA102/08/0442)
n3:dodaniDat
n13:2010
n3:domaciTvurceVysledku
n9:9818952 n9:5325773
n3:druhVysledku
n4:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
318380
n3:idVysledku
RIV/68407700:21230/09:00158517
n3:jazykVysledku
n21:cze
n3:klicovaSlova
System identification; grey box model; nonlinear least squares; Maximum likelihood
n3:klicoveSlovo
n12:System%20identification n12:grey%20box%20model n12:Maximum%20likelihood n12:nonlinear%20least%20squares
n3:kontrolniKodProRIV
[E2173C660761]
n3:mistoKonaniAkce
Stará Lesná, Vysoké Tatry
n3:mistoVydani
Košice
n3:nazevZdroje
Identifikace grey-box modelu s praktickou aplikací v průmyslu a energetice
n3:obor
n18:BC
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n8:GA102%2F08%2F0442
n3:rokUplatneniVysledku
n13:2009
n3:tvurceVysledku
Havlena, Vladimír Řehoř, Jiří
n3:typAkce
n5:WRD
n3:zahajeniAkce
2009-09-23+02:00
s:numberOfPages
5
n15:hasPublisher
Technická univerzita v Košiciach
n17:isbn
978-80-553-0237-9
n19:organizacniJednotka
21230