RBF Networks for function approximation in dynamic modelling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F09%3A00021344" target="_blank" >RIV/61989100:27240/09:00021344 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
RBF Networks for function approximation in dynamic modelling
Original language description
The paper demonstrates the comparison of Monte Carlo simulation algorithm with neural network enhancement in the reliability case study. With regard to process dynamics, we attempt to evaluate the tank system unreliability related to the initiative inputparameters setting. The neural network is used in equation coefficients calculation, which is executed in each transient state. Due to the neural networks, for some of the initial component settings we can achieve the results of computation faster thanin classical way of coefficients calculating and substituting into the equation.
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/1M06047" target="_blank" >1M06047: Research Center for Quality and Reliability of Production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Reliability and Risk Analysis: Theory and Applications
ISSN
1932-2321
e-ISSN
—
Volume of the periodical
2009
Issue of the periodical within the volume
No.2 (13)
Country of publishing house
US - UNITED STATES
Number of pages
160
Pages from-to
—
UT code for WoS article
—
EID of the result in the Scopus database
—