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Statements

Subject Item
n2:RIV%2F00216305%3A26210%2F09%3APU85803%21RIV10-MSM-26210___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differen Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differen
dcterms:title
Advanced approach to numerical forecasting using artificial neural networks Advanced approach to numerical forecasting using artificial neural networks
skos:prefLabel
Advanced approach to numerical forecasting using artificial neural networks Advanced approach to numerical forecasting using artificial neural networks
skos:notation
RIV/00216305:26210/09:PU85803!RIV10-MSM-26210___
n3:aktivita
n10:Z n10:P
n3:aktivity
P(GA102/07/1503), Z(MSM0021630529), Z(MSM6215648904)
n3:cisloPeriodika
6
n3:dodaniDat
n14:2010
n3:domaciTvurceVysledku
n19:1250019
n3:druhVysledku
n15:J
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n5:predkladatel
n3:idSjednocenehoVysledku
302032
n3:idVysledku
RIV/00216305:26210/09:PU85803
n3:jazykVysledku
n17:eng
n3:klicovaSlova
Artificial Neural Networks, Multi Layer Perceptron Network, Numerical Forecasting, Radial basis function
n3:klicoveSlovo
n8:Multi%20Layer%20Perceptron%20Network n8:Radial%20basis%20function n8:Numerical%20Forecasting n8:Artificial%20Neural%20Networks
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[09F08142A6A1]
n3:nazevZdroje
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
n3:obor
n12:JD
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:projekt
n13:GA102%2F07%2F1503
n3:rokUplatneniVysledku
n14:2009
n3:svazekPeriodika
2009
n3:tvurceVysledku
Štencl, Michael Šťastný, Jiří
n3:zamer
n16:MSM6215648904 n16:MSM0021630529
s:issn
1211-8516
s:numberOfPages
8
n9:organizacniJednotka
26210