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Elliott waves classification via softcomputing

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dc.title Elliott waves classification via softcomputing en
dc.contributor.author Volná, Eva
dc.contributor.author Kotyrba, Martin
dc.contributor.author Komínková Oplatková, Zuzana
dc.relation.ispartof MENDEL 2013
dc.identifier.issn 1803-3814 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 9788021447554
dc.date.issued 2013
dc.citation.spage 69
dc.citation.epage 74
dc.event.title 19th International Conference on Soft Computing: Evolutionary Computation, Genetic Programming, Swarm Intelligence, Fuzzy Logic, Neural Networks, Fractals, Bayesian Methods, MENDEL 2013
dc.event.location Brno
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2013-06-26
dc.event.edate 2013-06-28
dc.type conferenceObject
dc.language.iso en
dc.publisher Brno University of Technology
dc.subject Analytic programming en
dc.subject Backpropagation en
dc.subject Elliott waves en
dc.subject Levenberg-marquardt en
dc.subject Neural network en
dc.description.abstract This article presents a comparative study of the classification of Elliott waves in the chart. Regarding the methods of classification, the paper deals with backpropagation neural networks. Forms of activation functions of neurons in the hidden and output layers have been proposed by analytical programming with the differential evolution. The adaptation of the proposed neural network was performed by a standard backpropagation rule and Levenberg- Marquardt adaptation from Neural Network Toolbox in the Wolfram Mathematica environment. In conclusion, all results of this experimental study were evaluated and compared with each other. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004669
utb.identifier.obdid 43870812
utb.identifier.scopus 2-s2.0-84905750211
utb.source d-scopus
dc.date.accessioned 2015-06-04T12:54:47Z
dc.date.available 2015-06-04T12:54:47Z
utb.contributor.internalauthor Komínková Oplatková, Zuzana
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