Přístupnostní navigace
E-application
Search Search Close
Publication detail
NOVÁK, D. LEHKÝ, D.
Original Title
Inverse analysis based on Small-sample stochastic training of neural network
Type
conference paper
Language
English
Original Abstract
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.
Keywords
inverse analysis, Latin Hypercube Sampling, stochastic training of neural network, concrete
Authors
NOVÁK, D.; LEHKÝ, D.
RIV year
2005
Released
24. 8. 2005
Publisher
Stéphane Lecoeuche and Dimitris Tsaptsinos
Location
Lille, France
Pages from
155
Pages to
162
Pages count
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" }