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  • The paper suggests a new approach of inverse analysis to obtain parameters of FEM computational model in order to obtain best agreement witch experimental data. The proposed inverse analysis approach is based on coupling of FEM computational model and the stochastic training of artificial neural network. Identification parameters play the role of basic random variables witch a scatter reflecting the physical range of possible values. Novelty of the approach is the utilization of efficient small-sample ssimulation method Latin Hypercube Sampling (LHS) used for training of neural network.
  • The paper suggests a new approach of inverse analysis to obtain parameters of FEM computational model in order to obtain best agreement witch experimental data. The proposed inverse analysis approach is based on coupling of FEM computational model and the stochastic training of artificial neural network. Identification parameters play the role of basic random variables witch a scatter reflecting the physical range of possible values. Novelty of the approach is the utilization of efficient small-sample ssimulation method Latin Hypercube Sampling (LHS) used for training of neural network. (en)
  • The paper suggests a new approach of inverse analysis to obtain parameters of FEM computational model in order to obtain best agreement witch experimental data. The proposed inverse analysis approach is based on coupling of FEM computational model and the stochastic training of artificial neural network. Identification parameters play the role of basic random variables witch a scatter reflecting the physical range of possible values. Novelty of the approach is the utilization of efficient small-sample ssimulation method Latin Hypercube Sampling (LHS) used for training of neural network. (cs)
Title
  • Inverse FEM Analysis I: Stochastic Training of Neural Network
  • Inverse FEM Analysis I: Stochastic Training of Neural Network (en)
  • Inverse FEM Analysis I: Stochastic Training of Neural Network (cs)
skos:prefLabel
  • Inverse FEM Analysis I: Stochastic Training of Neural Network
  • Inverse FEM Analysis I: Stochastic Training of Neural Network (en)
  • Inverse FEM Analysis I: Stochastic Training of Neural Network (cs)
skos:notation
  • RIV/00216305:26110/05:PU54992!RIV06-GA0-26110___
http://linked.open.../vavai/riv/strany
  • 233-244
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA103/04/2092)
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
  • 525574
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26110/05:PU54992
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • inverse analysis, parameters, Small.sample simulation, Latin Hypercube Sampling, neural network (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [A6E151DA9048]
http://linked.open...v/mistoKonaniAkce
  • Svratka
http://linked.open...i/riv/mistoVydani
  • Svratka, Czech Republic
http://linked.open...i/riv/nazevZdroje
  • Inženýrská mechanika 2005
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
  • Lehký, David
  • Novák, Drahomír
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Neuveden
https://schema.org/isbn
  • 80-85918-93-5
http://localhost/t...ganizacniJednotka
  • 26110
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