Self-adaptive Artificial Neural Network in Numerical Models Calibration
Result description
The layered neural networks are considered as very general tools for approximation. In the presented contribution, a neural network with a very simple rule for the choice of an appropriate number of hidden neurons is applied to a material parameters' identification problem. Two identification strategies are compared. In the first one, the neural network is used to approximate the numerical model predicting the response for a given set of material parameters and loading. The second mode employs the neural network for constructing an inverse model, where material parameters are directly predicted for a given response.
Keywords
artificial neural networkmulti-layer perceptronapproximationnonlinear relationsback-propagationparameter identification
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Self-adaptive Artificial Neural Network in Numerical Models Calibration
Original language description
The layered neural networks are considered as very general tools for approximation. In the presented contribution, a neural network with a very simple rule for the choice of an appropriate number of hidden neurons is applied to a material parameters' identification problem. Two identification strategies are compared. In the first one, the neural network is used to approximate the numerical model predicting the response for a given set of material parameters and loading. The second mode employs the neural network for constructing an inverse model, where material parameters are directly predicted for a given response.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks - ICANN 2010
ISBN
978-3-642-15818-6
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
Springer-Verlag
Place of publication
Berlin
Event location
Thessaloniki
Event date
Sep 15, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000287889800045
Basic information
Result type
D - Article in proceedings
CEP
JD - Use of computers, robotics and its application
Year of implementation
2010