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Artificial neural networks in artificial time series prediction benchmark

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dc.title Artificial neural networks in artificial time series prediction benchmark en
dc.contributor.author Sámek, David
dc.contributor.author Maňas, David
dc.relation.ispartof International Journal of Mathematical Models and Methods in Applied Sciences
dc.identifier.issn 1998-0140 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2011
utb.relation.volume 5
utb.relation.issue 6
dc.citation.spage 1085
dc.citation.epage 1093
dc.type article
dc.language.iso en
dc.relation.uri http://www.naun.org/main/NAUN/ijmmas/20-869.pdf
dc.subject Artificial neural network en
dc.subject Benchmark en
dc.subject Prediction en
dc.subject Time series en
dc.description.abstract The work is aimed to research of predicting abilities of artificial neural networks. The characteristic samples of artificial neural network types were selected to be compared in numerous simulations, while influences of key parameters are studied. The tested artificial networks are as follows: multilayered feed-forward neural network, recurrent Elman neural network, adaptive linear network and radial basis function neural network. en
utb.faculty Faculty of Technology
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1002673
utb.identifier.rivid RIV/70883521:28110/11:43865533!RIV12-MSM-28110___
utb.identifier.rivid RIV/70883521:28140/11:43865533!RIV12-MSM-28140___
utb.identifier.obdid 43865545
utb.identifier.scopus 2-s2.0-79960302665
utb.source j-scopus
dc.date.accessioned 2012-02-10T13:15:28Z
dc.date.available 2012-02-10T13:15:28Z
utb.contributor.internalauthor Sámek, David
utb.contributor.internalauthor Maňas, David
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