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  • 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 a compariso
  • 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 a compariso (en)
Title
  • Application of Artificial Neural Networks and Regression for Analysis of Chemical Steel Reheating
  • Application of Artificial Neural Networks and Regression for Analysis of Chemical Steel Reheating (en)
skos:prefLabel
  • Application of Artificial Neural Networks and Regression for Analysis of Chemical Steel Reheating
  • Application of Artificial Neural Networks and Regression for Analysis of Chemical Steel Reheating (en)
skos:notation
  • RIV/61989100:27360/09:00022104!RIV10-MSM-27360___
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  • S
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  • 2
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  • 303749
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  • RIV/61989100:27360/09:00022104
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  • neural networks; prediction; model; metallurgy; steel (en)
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  • CZ - Česká republika
http://linked.open...ontrolniKodProRIV
  • [E2B625A5A1A6]
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  • Sborník vědeckých prací vysoké školy báňské-Technické univerzity Ostrava
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  • LV
http://linked.open...iv/tvurceVysledku
  • Jančíková, Zora
  • Zimný, Ondřej
  • Morávka, J.
issn
  • 1210-0471
number of pages
http://localhost/t...ganizacniJednotka
  • 27360
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