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Statements

Subject Item
n2:RIV%2F61989100%3A27360%2F11%3A86081883%21RIV12-MSM-27360___
rdf:type
skos:Concept n16:Vysledek
dcterms:description
With regards to many effects which can disrupt the delivery schedule of iron ore to a company, this raw material must be ordered well in advance. Determination of the required order volume results from prediction of iron ore demand. With respect to the fluctuations on the metallurgical commodity market, it is very difficult to use classical prediction models based on time series analysis. A typical example when models based solely on historical time series can not be used to predict iron ore demand is the world economic crisis period, because the demand for metallurgical commodities witnesses a sharp decrease. The article presents prediction model based on multilayer artificial neural network which takes into account not only historical data of iron ore demand but also information regarding the current situation on the world steel market and the iron ore stock volume of a given metallurgical company. The model is designed in such a way that the output is represented by the demand prediction of iron ore for the next month. Levenberg-Maguardt algorithm was used for learning the network. The proposed model should be included in the hybrid intelligent decision support system which will make it possible to efficiently reduce uncertainty and risk of logistics decision-making in the sphere of iron ore supply. With regards to many effects which can disrupt the delivery schedule of iron ore to a company, this raw material must be ordered well in advance. Determination of the required order volume results from prediction of iron ore demand. With respect to the fluctuations on the metallurgical commodity market, it is very difficult to use classical prediction models based on time series analysis. A typical example when models based solely on historical time series can not be used to predict iron ore demand is the world economic crisis period, because the demand for metallurgical commodities witnesses a sharp decrease. The article presents prediction model based on multilayer artificial neural network which takes into account not only historical data of iron ore demand but also information regarding the current situation on the world steel market and the iron ore stock volume of a given metallurgical company. The model is designed in such a way that the output is represented by the demand prediction of iron ore for the next month. Levenberg-Maguardt algorithm was used for learning the network. The proposed model should be included in the hybrid intelligent decision support system which will make it possible to efficiently reduce uncertainty and risk of logistics decision-making in the sphere of iron ore supply.
dcterms:title
Model of multilayer artificial neural network for prediction of iron ore demand Model of multilayer artificial neural network for prediction of iron ore demand
skos:prefLabel
Model of multilayer artificial neural network for prediction of iron ore demand Model of multilayer artificial neural network for prediction of iron ore demand
skos:notation
RIV/61989100:27360/11:86081883!RIV12-MSM-27360___
n16:predkladatel
n17:orjk%3A27360
n4:aktivita
n20:Z n20:S
n4:aktivity
S, Z(MSM6198910015)
n4:dodaniDat
n11:2012
n4:domaciTvurceVysledku
n9:9334130 n9:5673526
n4:druhVysledku
n13:D
n4:duvernostUdaju
n19:S
n4:entitaPredkladatele
n14:predkladatel
n4:idSjednocenehoVysledku
212897
n4:idVysledku
RIV/61989100:27360/11:86081883
n4:jazykVysledku
n6:eng
n4:klicovaSlova
artificial neural network; iron ore demand; prediction models
n4:klicoveSlovo
n15:iron%20ore%20demand n15:artificial%20neural%20network n15:prediction%20models
n4:kontrolniKodProRIV
[5F1EA6ED6F47]
n4:mistoKonaniAkce
Brno
n4:mistoVydani
Ostrava
n4:nazevZdroje
20th Anniversary International Conference on Metallurgy and Materials: METAL 2011
n4:obor
n21:AE
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
3
n4:rokUplatneniVysledku
n11:2011
n4:tvurceVysledku
Besta, Petr Feliks, J. Lenort, Radim
n4:typAkce
n22:WRD
n4:zahajeniAkce
2011-05-18+02:00
n4:zamer
n5:MSM6198910015
s:numberOfPages
5
n18:hasPublisher
Tanger s.r.o.
n8:isbn
978-80-87294-24-6
n12:organizacniJednotka
27360