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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 |