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Soft Computing
Erschienen in: Soft Computing 6/2018

03.03.2016 | Methodologies and Application

Elliott waves classification by means of neural and pseudo neural networks

verfasst von: Eva Volná, Martin Kotyrba, Zuzana Komínková Oplatková, Roman Senkerik

Erschienen in: Soft Computing | Ausgabe 6/2018

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Abstract

This article presents a comparative study of the classification of Elliott waves in data. Regarding the methods of classification, the paper deals with three approaches. The first one is a multilayer artificial neural network (ANN) with sigmoid activation function and backpropagation and Levenberg–Marquardt training algorithm. Second approach uses training algorithms of ANN but forms of activation functions of hidden nodes and nodes in output layers have been proposed by analytical programming with the differential evolution. The last approach offers results performed by synthesized pseudo neural networks where the symbolic regression is used for synthesis of a whole structure of the classifier, i.e., the relation between inputs and output(s) similar to ANN. In this case, meta-evolution version of analytic programming with differential evolution is used. In conclusion, all results of this experimental study were evaluated and compared mutually.
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Fußnoten
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Metadaten
Titel
Elliott waves classification by means of neural and pseudo neural networks
verfasst von
Eva Volná
Martin Kotyrba
Zuzana Komínková Oplatková
Roman Senkerik
Publikationsdatum
03.03.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 6/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2097-y

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