Detail publikace

Metodika použití umělých neuronových sítí pro identifikaci parametrů výpočtových modelů konstrukcí

NOVÁK, D. LEHKÝ, D.

Originální název

Metodika použití umělých neuronových sítí pro identifikaci parametrů výpočtových modelů konstrukcí

Český název

Metodika použití umělých neuronových sítí pro identifikaci parametrů výpočtových modelů konstrukcí

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

cs

Originální abstrakt

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.

Český abstrakt

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.

Rok RIV

2006

Vydáno

11.05.2006

Místo

Brno, ČR

ISBN

80-214-3164-4

Kniha

Dynamicky namáhané konstrukce - DYNA

Strany od

115

Strany do

122

Strany počet

8

BibTex


@inproceedings{BUT24290,
  author="Drahomír {Novák} and David {Lehký}",
  title="Metodika použití umělých neuronových sítí pro identifikaci parametrů výpočtových modelů konstrukcí",
  annote="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.",
  booktitle="Dynamicky namáhané konstrukce - DYNA",
  chapter="24290",
  year="2006",
  month="may",
  pages="115--122",
  type="conference paper"
}