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Description
  • Je prezentováno numerické řešení problému filtrace pro nelineární stochastické systémy. Cílem je vylepšení metody bodových mas pro multimodální hutsoty pravděpodobnosti stavu. Hlavním změny se týkají aktualizace sítě, jmenovitě pokrytí nezanedbatelných podpůrných a slučovacích sítí ve vícesíťovém návrhu. Při srovnání se standardním algoritmem bodových mas si nová metoda zachovává kvalitu odhadu při snížení výpočetních nároků pro multimodální hustoty. (cs)
  • Numerical solution of filtering problem for nonlinear stochastic systems is treated. The aim is to improve the point-mass method for multimodal probability density functions of state. The main innovation items concern grid update, namely covering a nonnegligible probability density function support and merging grids in multigrid design. Comparing to the standard point-mass algorithm, the new boundary-based grid placement technique maintains estimation quality and the merging technique decreases computational demands for multimodal densities.
  • Numerical solution of filtering problem for nonlinear stochastic systems is treated. The aim is to improve the point-mass method for multimodal probability density functions of state. The main innovation items concern grid update, namely covering a nonnegligible probability density function support and merging grids in multigrid design. Comparing to the standard point-mass algorithm, the new boundary-based grid placement technique maintains estimation quality and the merging technique decreases computational demands for multimodal densities. (en)
Title
  • Numerical Solution of Filtering Problem with Multimodal Densities
  • Numerical Solution of Filtering Problem with Multimodal Densities (en)
  • Numerické řešení problému filtrace s multimodálními hustotami (cs)
skos:prefLabel
  • Numerical Solution of Filtering Problem with Multimodal Densities
  • Numerical Solution of Filtering Problem with Multimodal Densities (en)
  • Numerické řešení problému filtrace s multimodálními hustotami (cs)
skos:notation
  • RIV/49777513:23520/06:00000429!RIV07-MSM-23520___
http://linked.open.../vavai/riv/strany
  • 242-247
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM 235200004)
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
  • 489397
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/06:00000429
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • stochastic systems; state estimation; nonlinear filters; probability density function; estimation algorithms (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [45456CFBF88B]
http://linked.open...i/riv/mistoVydani
  • Oxford
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 16th IFAC World Congress
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Šimandl, Miroslav
  • Královec, Jakub
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Elsevier
https://schema.org/isbn
  • 0-08-045108-X
http://localhost/t...ganizacniJednotka
  • 23520
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