Application of Artificial Neural Networks and Regression for Analysis of Chemical Steel Reheating

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F09%3A00022104" target="_blank" >RIV/61989100:27360/09:00022104 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Application of Artificial Neural Networks and Regression for Analysis of Chemical Steel Reheating

Popis výsledku v původním jazyce

Before realization of these systems control requested by practice it is necessary to execute their structural and parametric identification. As these processes are very complex, all exact relations for their mathematical description are not known so far.Some metallurgical systems are practically non-described so far (black box), further described only partially (grey box), while only a little of them are described almost fully (white box). Identification by means of artificial neural networks enables rather external system description (i.e. black box models creation), when we get an acceptable accordance between real and modeled outputs, i.e. so called output estimation (prediction). This approach is thus more suitable for control than for identification itself. Contribution deals with a possibility of prediction of a temperature after a steel chemical heating on device of integrated system of secondary metallurgy by means of regression analysis and artificial neural networks and with

Název v anglickém jazyce

Application of Artificial Neural Networks and Regression for Analysis of Chemical Steel Reheating

Popis výsledku anglicky

Before realization of these systems control requested by practice it is necessary to execute their structural and parametric identification. As these processes are very complex, all exact relations for their mathematical description are not known so far.Some metallurgical systems are practically non-described so far (black box), further described only partially (grey box), while only a little of them are described almost fully (white box). Identification by means of artificial neural networks enables rather external system description (i.e. black box models creation), when we get an acceptable accordance between real and modeled outputs, i.e. so called output estimation (prediction). This approach is thus more suitable for control than for identification itself. Contribution deals with a possibility of prediction of a temperature after a steel chemical heating on device of integrated system of secondary metallurgy by means of regression analysis and artificial neural networks and with

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JG - Hutnictví, kovové materiály

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

Sborník vědeckých prací vysoké školy báňské-Technické univerzity Ostrava

ISSN

1210-0471

e-ISSN

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Svazek periodika

LV

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

6

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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