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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. (en)
Title
  • Knowledge Elicitation Via Extension of Fragmental Knowledge Pieces
  • Knowledge Elicitation Via Extension of Fragmental Knowledge Pieces (en)
skos:prefLabel
  • Knowledge Elicitation Via Extension of Fragmental Knowledge Pieces
  • Knowledge Elicitation Via Extension of Fragmental Knowledge Pieces (en)
skos:notation
  • RIV/67985556:_____/09:00328657!RIV10-AV0-67985556
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/08/0567), Z(AV0Z10750506)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 321921
http://linked.open...ai/riv/idVysledku
  • RIV/67985556:_____/09:00328657
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • knowledge elicitation; Bayesian paradigm (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [3ACCB6BF0910]
http://linked.open...v/mistoKonaniAkce
  • Budapest
http://linked.open...i/riv/mistoVydani
  • Budapest
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the European Control Conference 2009
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Kárný, Miroslav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • European Union Control Association
https://schema.org/isbn
  • 978-963-311-369-1
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