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Statements

Subject Item
n2:RIV%2F68407700%3A21110%2F07%3A01131834%21RIV08-MSM-21110___
rdf:type
skos:Concept n16:Vysledek
dcterms:description
V tomto příspěvku je představena metoda identifikace parametrů mikroploškového modelu M4 pro beton založená na vrstvených neuronových sítích. 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.
dcterms:title
Back Analysis of Microplane Model Parameters Using Soft Computing Methods Metoda identifikace parametrů mikroploškového modelu založená na vrstvených neuronových sítích Back Analysis of Microplane Model Parameters Using Soft Computing Methods
skos:prefLabel
Back Analysis of Microplane Model Parameters Using Soft Computing Methods Back Analysis of Microplane Model Parameters Using Soft Computing Methods Metoda identifikace parametrů mikroploškového modelu založená na vrstvených neuronových sítích
skos:notation
RIV/68407700:21110/07:01131834!RIV08-MSM-21110___
n3:strany
219;242
n3:aktivita
n9:Z
n3:aktivity
Z(MSM6840770003)
n3:cisloPeriodika
2
n3:dodaniDat
n4:2008
n3:domaciTvurceVysledku
n5:5131391 n5:9081429 n5:4695046
n3:druhVysledku
n15:J
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
411269
n3:idVysledku
RIV/68407700:21110/07:01131834
n3:jazykVysledku
n13:eng
n3:klicovaSlova
Global optimization; Inverse analysis; Layered neural networks; Microplane models; Stochastic sensitivity
n3:klicoveSlovo
n6:Stochastic%20sensitivity n6:Inverse%20analysis n6:Global%20optimization n6:Microplane%20models n6:Layered%20neural%20networks
n3:kodStatuVydavatele
PL - Polská republika
n3:kontrolniKodProRIV
[A7C154E80A1B]
n3:nazevZdroje
Computer Assisted Mechanics and Engineering Sciences
n3:obor
n18:JI
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n4:2007
n3:svazekPeriodika
7
n3:tvurceVysledku
Kučerová, Anna Lepš, Matěj Zeman, Jan
n3:zamer
n11:MSM6840770003
s:issn
1232-308X
s:numberOfPages
24
n14:organizacniJednotka
21110