Failure and Power Utilization System Models of Differential Equations by Polynomial Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F13%3A86087577" target="_blank" >RIV/61989100:27740/13:86087577 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.softcomputing.net/paper61.pdf" target="_blank" >http://www.softcomputing.net/paper61.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Failure and Power Utilization System Models of Differential Equations by Polynomial Neural Networks

Popis výsledku v původním jazyce

Reliability modeling of electronic circuits can be best performed by the stressor ? susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. Complex manufacturing systems often require a high level of reliability from the incoming electricity supply. Modern industrial time power quality monitoring systems can be used for the pre-fault load alarming. Neural networks can successfully model and predict the failure frame of critical electronic systems and power utilization in power plants described only a few input quantities. Differential polynomial neural network is a new type of neural network, which constructs and substitutes an unknown general sum partial differential equation with a total sum of fractional polynomial terms. The system model describes partial relative derivative dependent changes of some input combinations of variables. This type of non-linear regression is based on trained generalized dat

Název v anglickém jazyce

Failure and Power Utilization System Models of Differential Equations by Polynomial Neural Networks

Popis výsledku anglicky

Reliability modeling of electronic circuits can be best performed by the stressor ? susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. Complex manufacturing systems often require a high level of reliability from the incoming electricity supply. Modern industrial time power quality monitoring systems can be used for the pre-fault load alarming. Neural networks can successfully model and predict the failure frame of critical electronic systems and power utilization in power plants described only a few input quantities. Differential polynomial neural network is a new type of neural network, which constructs and substitutes an unknown general sum partial differential equation with a total sum of fractional polynomial terms. The system model describes partial relative derivative dependent changes of some input combinations of variables. This type of non-linear regression is based on trained generalized dat

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0016" target="_blank" >EE2.3.30.0016: Příležitost pro mladé výzkumníky</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2013 Thirteenth International Conference on Hybrid Intelligent Systems (HIS 2013) : Yassmine Hammamet, Tunisia, 04-06 December, 2013

ISBN

978-1-4799-2439-4

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

274-279

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Yassmine Hammamet

Datum konání akce

4. 12. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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