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NOVÁK, D. LEHKÝ, D.
Original Title
Metodika použití umělých neuronových sítí pro identifikaci parametrů výpočtových modelů konstrukcí
English Title
Methodology of using artificial neural networks for identification of computational model parameters
Type
conference paper
Language
Czech
Original Abstract
The paper suggests a new approach of inverse analysis to obtain parameters of FEM computational model in order to obtain best agreement with experimental data. The proposed inverse analysis approach is based on coupling of FEM computational model and the stochastic training of artificial neural network. Identification parameters play the role of basic random variables with a scatter reflecting the physical range of possible values. Novelty of the approach is the utilization of efficient small-sample simulation method Latin Hypercube Sampling (LHS) used for stochastic training of neural network. Once the network is trained it represents an approximation consequently utilized in an opposite way: For given experimental data to provide the best possible set of model parameters. The approach is general and can be applied easily to any inverse problem of engineering mechanics.
English abstract
Key words in English
Inverse analysis, computational model, stochastic neural network, small-sample simulation.
Authors
NOVÁK, D.; LEHKÝ, D.
RIV year
2006
Released
11. 5. 2006
Location
Brno, ČR
ISBN
80-214-3164-4
Book
Dynamicky namáhané konstrukce - DYNA
Pages from
115
Pages to
122
Pages count
8
BibTex
@inproceedings{BUT24290, author="Drahomír {Novák} and David {Lehký}", title="Metodika použití umělých neuronových sítí pro identifikaci parametrů výpočtových modelů konstrukcí", booktitle="Dynamicky namáhané konstrukce - DYNA", year="2006", pages="115--122", address="Brno, ČR", isbn="80-214-3164-4" }