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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F09%3A00328657%21RIV10-AV0-67985556
rdf:type
skos:Concept n15:Vysledek
dcterms:description
The paper describes an advanced methodology of automatic knowledge elicitation. It merges fragmental uncertain knowledge pieces into the prior distribution of unknown parameter of a probabilistic model of a dynamic system. Careful knowledge elicitation helps in achieving as bump-less start of model-based controllers as possible. It is also important when observed data are poorly informative, which is a typical situation in closed control loops. Rigorous use of the Bayesian paradigm to the knowledge elicitation forms the essence of the methodology. Unlike former solutions, it can handle fragmental and incompletely compatible knowledge pieces in a systematic way. The description of the methodology dominates the paper and just an illustrative example is presented. The paper describes an advanced methodology of automatic knowledge elicitation. It merges fragmental uncertain knowledge pieces into the prior distribution of unknown parameter of a probabilistic model of a dynamic system. Careful knowledge elicitation helps in achieving as bump-less start of model-based controllers as possible. It is also important when observed data are poorly informative, which is a typical situation in closed control loops. Rigorous use of the Bayesian paradigm to the knowledge elicitation forms the essence of the methodology. Unlike former solutions, it can handle fragmental and incompletely compatible knowledge pieces in a systematic way. The description of the methodology dominates the paper and just an illustrative example is presented.
dcterms:title
Knowledge Elicitation Via Extension of Fragmental Knowledge Pieces Knowledge Elicitation Via Extension of Fragmental Knowledge Pieces
skos:prefLabel
Knowledge Elicitation Via Extension of Fragmental Knowledge Pieces Knowledge Elicitation Via Extension of Fragmental Knowledge Pieces
skos:notation
RIV/67985556:_____/09:00328657!RIV10-AV0-67985556
n3:aktivita
n6:P n6:Z
n3:aktivity
P(GA102/08/0567), Z(AV0Z10750506)
n3:dodaniDat
n4:2010
n3:domaciTvurceVysledku
n8:6585256
n3:druhVysledku
n14:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
321921
n3:idVysledku
RIV/67985556:_____/09:00328657
n3:jazykVysledku
n13:eng
n3:klicovaSlova
knowledge elicitation; Bayesian paradigm
n3:klicoveSlovo
n11:Bayesian%20paradigm n11:knowledge%20elicitation
n3:kontrolniKodProRIV
[3ACCB6BF0910]
n3:mistoKonaniAkce
Budapest
n3:mistoVydani
Budapest
n3:nazevZdroje
Proceedings of the European Control Conference 2009
n3:obor
n17:BB
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n9:GA102%2F08%2F0567
n3:rokUplatneniVysledku
n4:2009
n3:tvurceVysledku
Kárný, Miroslav
n3:typAkce
n16:WRD
n3:zahajeniAkce
2009-08-23+02:00
n3:zamer
n12:AV0Z10750506
s:numberOfPages
5
n21:hasPublisher
European Union Control Association
n18:isbn
978-963-311-369-1