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NOVÁK, D. LEHKÝ, D.
Original Title
ANN inverse analysis based on stochastic small-sample training set simulation
Type
journal article - other
Language
English
Original Abstract
A new approach of inverse analysis is proposed to obtain parameters of a computational model in order to achieve the best agreement with experimental data. The inverse analysis is based on the coupling of a stochastic simulation and an artificial neural network (ANN). The identification parameters play the role of basic random variables with a scater reflecting the physical range of potential values. A nonovelty of the approach is the utilization of the efficient small-sample simulation method LatinHypercube Sampling (LHS) used for the stochastic preparation of the training set utilized in training the artificial neural network.
Keywords
Inverse analysis, identification, Latin hypercube sampling, artifical neural network, concrete
Authors
NOVÁK, D.; LEHKÝ, D.
RIV year
2006
Released
19. 5. 2006
Publisher
Elsevier
Location
Velká Británie
ISBN
0952-1976
Periodical
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Year of study
19
Number
5
State
United Kingdom of Great Britain and Northern Ireland
Pages from
731
Pages to
740
Pages count
10
BibTex
@article{BUT44443, author="Drahomír {Novák} and David {Lehký}", title="ANN inverse analysis based on stochastic small-sample training set simulation", journal="ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE", year="2006", volume="19", number="5", pages="731--740", issn="0952-1976" }