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NOVÁK, D. LEHKÝ, D.
Originální název
Inverse analysis based on Small-sample stochastic training of neural network
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
The paper suggests a new approach of inverse analysis to obtain parameters of a computational model in order to obtain best agreement witch experimental data. The proposed inverse analysis approach is based on coupling of a computational model and the stochastic training of artificial neural network. The identification parameters play the role of basic random variables with a scatter reflecting the physical range of possible values. A novelty of the approach is the utilization of efficient small-sample simulation method Latin Hypercube Sampling (LHS) used for preparation of training set utilized in stochastic training of neural network. Once the network is trained it represented an approximation consequently utilized in an opposite way: For the given experimental data to provide the best possible set of model parameters.
Klíčová slova
inverse analysis, Latin Hypercube Sampling, stochastic training of neural network, concrete
Autoři
NOVÁK, D.; LEHKÝ, D.
Rok RIV
2005
Vydáno
24. 8. 2005
Nakladatel
Stéphane Lecoeuche and Dimitris Tsaptsinos
Místo
Lille, France
Strany od
155
Strany do
162
Strany počet
8
BibTex
@inproceedings{BUT21414, author="Drahomír {Novák} and David {Lehký}", title="Inverse analysis based on Small-sample stochastic training of neural network", booktitle="Novel Applications of Neural Network in Engineering", year="2005", pages="155--162", publisher="Stéphane Lecoeuche and Dimitris Tsaptsinos", address="Lille, France" }