Use of Neural Networks for Lifetime Analysis of Teeming Ladles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27710%2F22%3A10251121" target="_blank" >RIV/61989100:27710/22:10251121 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27360/22:10251121

Výsledek na webu

<a href="https://www.mdpi.com/1956460" target="_blank" >https://www.mdpi.com/1956460</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ma15228234" target="_blank" >10.3390/ma15228234</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Use of Neural Networks for Lifetime Analysis of Teeming Ladles

Popis výsledku v původním jazyce

When describing the behaviour and modelling of real systems, which are characterized by considerable complexity, great difficulty, and often the impossibility of their formal mathematical description, and whose operational monitoring and measurement are difficult, conventional analytical-statistical models run into the limits of their use. The application of these models leads to necessary simplifications, which cause insufficient adequacy of the resulting mathematical description. In such cases, it is appropriate for modelling to use the methods brought by a new scientific discipline-artificial intelligence. Artificial intelligence provides very promising tools for describing and controlling complex systems. The method of neural networks was chosen for the analysis of the lifetime of the teeming ladle. Artificial neural networks are mathematical models that approximate non-linear functions of an arbitrary waveform. The advantage of neural networks is their ability to generalize the dependencies between individual quantities by learning the presented patterns. This property of a neural network is referred to as generalization. Their use is suitable for processing complex problems where the dependencies between individual quantities are not exactly known.

Název v anglickém jazyce

Use of Neural Networks for Lifetime Analysis of Teeming Ladles

Popis výsledku anglicky

When describing the behaviour and modelling of real systems, which are characterized by considerable complexity, great difficulty, and often the impossibility of their formal mathematical description, and whose operational monitoring and measurement are difficult, conventional analytical-statistical models run into the limits of their use. The application of these models leads to necessary simplifications, which cause insufficient adequacy of the resulting mathematical description. In such cases, it is appropriate for modelling to use the methods brought by a new scientific discipline-artificial intelligence. Artificial intelligence provides very promising tools for describing and controlling complex systems. The method of neural networks was chosen for the analysis of the lifetime of the teeming ladle. Artificial neural networks are mathematical models that approximate non-linear functions of an arbitrary waveform. The advantage of neural networks is their ability to generalize the dependencies between individual quantities by learning the presented patterns. This property of a neural network is referred to as generalization. Their use is suitable for processing complex problems where the dependencies between individual quantities are not exactly known.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20500 - Materials engineering

Projekt

<a href="/cs/project/FW01010097" target="_blank" >FW01010097: Automatizované řídicí systémy v oblasti pánvové metalurgie</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Materials

ISSN

1996-1944

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

22

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

15

Strana od-do

nestrankovano

Kód UT WoS článku

000887369200001

EID výsledku v databázi Scopus

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