Self-adaptive Artificial Neural Network in Numerical Models Calibration
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F10%3A00176025" target="_blank" >RIV/68407700:21110/10:00176025 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
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
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
—
Result continuities
Project
—
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
—
e-ISSN
—
Number of pages
4
Pages from-to
—
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