The Usage of Regression Analysis and Artificial Intelligence Tools in The Field of Metallurgy

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The Usage of Regression Analysis and Artificial Intelligence Tools in The Field of Metallurgy

Popis výsledku v původním jazyce

The presented article deals with the possibility of using artificial intelligence tools, specifically genetic algorithms, in the metallurgical industry. The genetic algorithms are used here to estimate coefficients of regression functions. In some cases, the standard regression analysis tools lead to incorrect results. And then the genetic algorithms can serve as an optimization tool searching for a certain state space to find functions or functionalities optimum. To verify the correctness of this premise we have used the genetic algorithms to find coefficients of two regression functions types, namely linear regression function and nonlinear regression function. By comparing the results, we have confirmed in the discussion the suitability of using genetic algorithms in the field of regression analysis as well. Another result of this work is the determination of the general cost equation of the foundry furnaces. The data of selected foundries have been used both in classical regression analysis and in genetic algorithms applications.

Název v anglickém jazyce

The Usage of Regression Analysis and Artificial Intelligence Tools in The Field of Metallurgy

Popis výsledku anglicky

The presented article deals with the possibility of using artificial intelligence tools, specifically genetic algorithms, in the metallurgical industry. The genetic algorithms are used here to estimate coefficients of regression functions. In some cases, the standard regression analysis tools lead to incorrect results. And then the genetic algorithms can serve as an optimization tool searching for a certain state space to find functions or functionalities optimum. To verify the correctness of this premise we have used the genetic algorithms to find coefficients of two regression functions types, namely linear regression function and nonlinear regression function. By comparing the results, we have confirmed in the discussion the suitability of using genetic algorithms in the field of regression analysis as well. Another result of this work is the determination of the general cost equation of the foundry furnaces. The data of selected foundries have been used both in classical regression analysis and in genetic algorithms applications.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20501 - Materials engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

METAL 2017: conference proceedings : 26th International Conference on Metallurgy and Materials : (reviewed version) : May 24th-26th 2017, Hotel Voroněž I, Brno, Czech Republic, EU

ISBN

978-80-87294-79-6

ISSN

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e-ISSN

neuvedeno

Počet stran výsledku

6

Strana od-do

2063-2068

Název nakladatele

Tanger

Místo vydání

Ostrava

Místo konání akce

Brno

Datum konání akce

24. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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