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Statements

Subject Item
n2:RIV%2F61989100%3A27360%2F13%3A86088979%21RIV14-MSM-27360___
rdf:type
n14:Vysledek skos:Concept
dcterms: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. 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.
dcterms:title
Prediction of Metal Corrosion by Neural Networks Prediction of Metal Corrosion by Neural Networks
skos:prefLabel
Prediction of Metal Corrosion by Neural Networks Prediction of Metal Corrosion by Neural Networks
skos:notation
RIV/61989100:27360/13:86088979!RIV14-MSM-27360___
n14:predkladatel
n16:orjk%3A27360
n3:aktivita
n10:I
n3:aktivity
I
n3:cisloPeriodika
52
n3:dodaniDat
n15:2014
n3:domaciTvurceVysledku
n12:8578834 n12:2407558 n12:9573410
n3:druhVysledku
n9:J
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
98592
n3:idVysledku
RIV/61989100:27360/13:86088979
n3:jazykVysledku
n7:eng
n3:klicovaSlova
model; prediction; atmospheric corrosion; artificial neural networks
n3:klicoveSlovo
n4:model n4:atmospheric%20corrosion n4:artificial%20neural%20networks n4:prediction
n3:kodStatuVydavatele
HR - Chorvatská republika
n3:kontrolniKodProRIV
[FC2B69E88867]
n3:nazevZdroje
Metalurgija = Metallurgy
n3:obor
n17:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n15:2013
n3:svazekPeriodika
3
n3:tvurceVysledku
Jančíková, Zora Koštial, Pavol Zimný, Ondřej
s:issn
0543-5846
s:numberOfPages
3
n18:organizacniJednotka
27360