Methodology of using artificial neural networks for identification of computational model parameters

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F06%3APU65609" target="_blank" >RIV/00216305:26110/06:PU65609 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

čeština

Original language name

Metodika použití umělých neuronových sítí pro identifikaci parametrů výpočtových modelů konstrukcí

Original language description

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 thestochastic 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.

Czech name

Metodika použití umělých neuronových sítí pro identifikaci parametrů výpočtových modelů konstrukcí

Czech description

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 thestochastic 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.

Type

D - Article in proceedings

CEP classification

JN - Civil engineering

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2006

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Dynamicky namáhané konstrukce - DYNA

ISBN

80-214-3164-4

ISSN

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e-ISSN

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Number of pages

8

Pages from-to

115-122

Publisher name

Neuveden

Place of publication

Brno, ČR

Event location

Brno

Event date

May 11, 2006

Type of event by nationality

CST - Celostátní akce

UT code for WoS article

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