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Comparison of artificial neural networks using prediction benchmarking

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dc.title Comparison of artificial neural networks using prediction benchmarking en
dc.contributor.author Sámek, David
dc.contributor.author Maňas, David
dc.relation.ispartof Recent Researches in Automatic Control - 13th WSEAS International Conference on Automatic Control, Modelling and Simulation, ACMOS'11
dc.identifier.isbn 978-1-61804-004-6
dc.date.issued 2011
dc.citation.spage 152
dc.citation.epage 157
dc.event.title 13th WSEAS International Conference on Automatic Control, Modelling and Simulation, ACMOS'11
dc.event.location Lanzarote, Canary Islands
utb.event.state-en Spain
utb.event.state-cs Španělsko
dc.event.sdate 2011-05-27
dc.event.edate 2011-05-29
dc.type conferenceObject
dc.language.iso en
dc.relation.uri http://www.wseas.us/e-library/conferences/2011/Lanzarote/ACMOS/ACMOS-27.pdf
dc.subject Artificial neural network en
dc.subject Benchmark en
dc.subject Prediction en
dc.subject Time series en
dc.description.abstract Artificial neural networks are commonly used for prediction of various time series, linear and nonlinear systems. Nevertheless, the choice of proper type of artificial neural networks is difficult task, because each class of artificial neural networks has different features and abilities. Aim of this paper is to compare and benchmark four typical categories of artificial neural networks in artificial time series prediction and provide suggestions for this kind of applications. en
utb.faculty Faculty of Technology
dc.identifier.uri http://hdl.handle.net/10563/1004789
utb.identifier.obdid 43866831
utb.identifier.scopus 2-s2.0-82555178561
utb.source d-scopus
dc.date.accessioned 2015-06-04T12:55:26Z
dc.date.available 2015-06-04T12:55:26Z
utb.contributor.internalauthor Sámek, David
utb.contributor.internalauthor Maňas, David
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