About: Identification of nonlinear non-gaussian systems by neural networks     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • Článek je zaměřen na problém aplikace neuronových sítí v identifikaci nelineárních negaussovských systémů. Důraz je kladen na odhad parametrů neuronové sítě, které jsou trénovány využitím metody Gaussovských směsí, což je jedna z globálních filtračních metod, které umožňují určit pravděpodobnostní hustotní funkci vah sítě. Navržený postup odhadu parametrů (vah) sítě založeý na metodě Gaussovských směsí překonává obvykle používané metody chyby predikce a představuje zajímavou alternativu k sekvenčním metodám Monte Carlo. Navržený přístup trénování je demonstrován v ilustračním příkladě. (cs)
  • Application of neural networks in identification of nonlinear non-Gaussian systems is treated. Stress is laid on a parameter estimation of the networks. They are trained by the Gaussian sum method which is a global filtering method allowing to determine conditional probability density functions of network weights. Proposed approach to estimation of network weights (parameters) based on Gaussian sum filtering method overcomes commonly used prediction error methods and it is an interesting alternative to sequential Monte Carlo methods. The considered training approach is demonstrated by an illustration example.
  • Application of neural networks in identification of nonlinear non-Gaussian systems is treated. Stress is laid on a parameter estimation of the networks. They are trained by the Gaussian sum method which is a global filtering method allowing to determine conditional probability density functions of network weights. Proposed approach to estimation of network weights (parameters) based on Gaussian sum filtering method overcomes commonly used prediction error methods and it is an interesting alternative to sequential Monte Carlo methods. The considered training approach is demonstrated by an illustration example. (en)
Title
  • Identification of nonlinear non-gaussian systems by neural networks
  • Identifikace nelineárních negaussovských systémů neuronovými sítěmi (cs)
  • Identification of nonlinear non-gaussian systems by neural networks (en)
skos:prefLabel
  • Identification of nonlinear non-gaussian systems by neural networks
  • Identifikace nelineárních negaussovských systémů neuronovými sítěmi (cs)
  • Identification of nonlinear non-gaussian systems by neural networks (en)
skos:notation
  • RIV/49777513:23520/05:00000357!RIV07-MSM-23520___
http://linked.open.../vavai/riv/strany
  • 1307-1312
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
  • 524128
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/05:00000357
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • system identification; nonlinear non-Gaussian stochastic system; non-Gaussian disturbance; neural network training; Gaussian sum; Bayesian relations; multilayer perceptron network (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [40D22BECA897]
http://linked.open...i/riv/mistoVydani
  • Oxford
http://linked.open...i/riv/nazevZdroje
  • Nonlinear control systems 2004
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Hering, Pavel
  • Šimandl, Miroslav
  • Král, Ladislav
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Elsevier
https://schema.org/isbn
  • 0-08-044303-6
http://localhost/t...ganizacniJednotka
  • 23520
is http://linked.open...avai/riv/vysledek of
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software