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  • The contribution deals with the use of artificial neural networks for prediction of steel atmospheric corrosion. Atmospheric corrosion of metal materials exposed under atmospheric conditions depends on various factors such as local temperature, relative humidity, amount of precipitation, pH of rainfall, concentration of main pollutants and exposition time. As these factors are very complex, exact relation for mathematical description of atmospheric corrosion of various metals are not known so far. Classical analytical and mathematical functions are of limited use to describe this type of strongly non-linear system depending on various meteorological-chemical factors and interaction between them and on material parameters. Nowadays there is certain chance to predict a corrosion loss of materials by artificial neural networks. Neural networks are used primarily in real systems, which are characterized by high nonlinearity, considerable complexity and great difficulty of their formal mathematical description.
  • The contribution deals with the use of artificial neural networks for prediction of steel atmospheric corrosion. Atmospheric corrosion of metal materials exposed under atmospheric conditions depends on various factors such as local temperature, relative humidity, amount of precipitation, pH of rainfall, concentration of main pollutants and exposition time. As these factors are very complex, exact relation for mathematical description of atmospheric corrosion of various metals are not known so far. Classical analytical and mathematical functions are of limited use to describe this type of strongly non-linear system depending on various meteorological-chemical factors and interaction between them and on material parameters. Nowadays there is certain chance to predict a corrosion loss of materials by artificial neural networks. Neural networks are used primarily in real systems, which are characterized by high nonlinearity, considerable complexity and great difficulty of their formal mathematical description. (en)
Title
  • Prediction of Metal Corrosion by Neural Networks
  • Prediction of Metal Corrosion by Neural Networks (en)
skos:prefLabel
  • Prediction of Metal Corrosion by Neural Networks
  • Prediction of Metal Corrosion by Neural Networks (en)
skos:notation
  • RIV/61989100:27360/13:86088979!RIV14-MSM-27360___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • I
http://linked.open...iv/cisloPeriodika
  • 52
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 98592
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27360/13:86088979
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • model; prediction; atmospheric corrosion; artificial neural networks (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • HR - Chorvatská republika
http://linked.open...ontrolniKodProRIV
  • [FC2B69E88867]
http://linked.open...i/riv/nazevZdroje
  • Metalurgija = Metallurgy
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 3
http://linked.open...iv/tvurceVysledku
  • Jančíková, Zora
  • Koštial, Pavol
  • Zimný, Ondřej
issn
  • 0543-5846
number of pages
http://localhost/t...ganizacniJednotka
  • 27360
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