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  • A new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis and a genetic algorithm-based training of a neural network by an evolutionary algorithm. Advantages and disadvantages of this approach together with possible extensions are thoroughly discussed and analyzed.
  • A new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis and a genetic algorithm-based training of a neural network by an evolutionary algorithm. Advantages and disadvantages of this approach together with possible extensions are thoroughly discussed and analyzed. (en)
  • V tomto příspěvku je představena metoda identifikace parametrů mikroploškového modelu M4 pro beton založená na vrstvených neuronových sítích. (cs)
Title
  • Back Analysis of Microplane Model Parameters Using Soft Computing Methods
  • Back Analysis of Microplane Model Parameters Using Soft Computing Methods (en)
  • Metoda identifikace parametrů mikroploškového modelu založená na vrstvených neuronových sítích (cs)
skos:prefLabel
  • Back Analysis of Microplane Model Parameters Using Soft Computing Methods
  • Back Analysis of Microplane Model Parameters Using Soft Computing Methods (en)
  • Metoda identifikace parametrů mikroploškového modelu založená na vrstvených neuronových sítích (cs)
skos:notation
  • RIV/68407700:21110/07:01131834!RIV08-MSM-21110___
http://linked.open.../vavai/riv/strany
  • 219;242
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770003)
http://linked.open...iv/cisloPeriodika
  • 2
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
  • 411269
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21110/07:01131834
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Global optimization; Inverse analysis; Layered neural networks; Microplane models; Stochastic sensitivity (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • PL - Polská republika
http://linked.open...ontrolniKodProRIV
  • [A7C154E80A1B]
http://linked.open...i/riv/nazevZdroje
  • Computer Assisted Mechanics and Engineering Sciences
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 7
http://linked.open...iv/tvurceVysledku
  • Kučerová, Anna
  • Lepš, Matěj
  • Zeman, Jan
http://linked.open...n/vavai/riv/zamer
issn
  • 1232-308X
number of pages
http://localhost/t...ganizacniJednotka
  • 21110
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