ANN inverse analysis based on stochastic small-sample training set simulation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F06%3APU65604" target="_blank" >RIV/00216305:26110/06:PU65604 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

ANN inverse analysis based on stochastic small-sample training set simulation

Original language description

A new approach of inverse analysis is proposed to obtain parameters of a computational model in order to achieve the best agreement with experimental data. The inverse analysis is based on the coupling of a stochastic simulation and an artificial neuralnetwork (ANN). The identification parameters play the role of basic random variables with a scater reflecting the physical range of potential values. A nonovelty of the approach is the utilization of the efficient small-sample simulation method LatinHypercube Sampling (LHS) used for the stochastic preparation of the training set utilized in training the artificial neural network.

Czech name

ANN inverse analysis based on stochastic small-sample training set simulation

Czech description

A new approach of inverse analysis is proposed to obtain parameters of a computational model in order to achieve the best agreement with experimental data. The inverse analysis is based on the coupling of a stochastic simulation and an artificial neuralnetwork (ANN). The identification parameters play the role of basic random variables with a scater reflecting the physical range of potential values. A nonovelty of the approach is the utilization of the efficient small-sample simulation method LatinHypercube Sampling (LHS) used for the stochastic preparation of the training set utilized in training the artificial neural network.

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JN - Civil engineering

OECD FORD branch

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Project

<a href="/en/project/GA103%2F04%2F2092" target="_blank" >GA103/04/2092: Model identification and optimization at material a structural levels</a><br>

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

Name of the periodical

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

ISSN

0952-1976

e-ISSN

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Volume of the periodical

19

Issue of the periodical within the volume

5

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

731-740

UT code for WoS article

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EID of the result in the Scopus database

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