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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Acknowledgements
The research described here has been financially supported by University of Ostrava grant SGS17/PřF/2015, it was also supported by Grant Agency of the Czech Republic—GACR P103/15/06700S, further by financial support of research project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.
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Volná, E., Kotyrba, M., Oplatková, Z.K. et al. Elliott waves classification by means of neural and pseudo neural networks. Soft Comput 22, 1803–1813 (2018). https://doi.org/10.1007/s00500-016-2097-y
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DOI: https://doi.org/10.1007/s00500-016-2097-y