Cascade neo-fuzzy neural network in the forecasting problem at stock exchange

Authors

  • Yuriy P. Zaychenko The Educational and Scientific Complex "Institute for Applied System Analysis" of National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine https://orcid.org/0000-0001-9662-3269
  • Galib Hamidov The Information Technologies Department of Azerbaijanenergo, Baku, Azerbaijan

DOI:

https://doi.org/10.20535/SRIT.2308-8893.2017.2.09

Keywords:

forecasting, stock exchange, cascade neo-fuzzy neural network, FNN ANFIS

Abstract

A forecasting problem at the stock exchange is considered. For its solution the application of a cascade neo-fuzzy neural network (CNFNN) is suggested. The architecture of the neo-fuzzy neuron and architecture of CNFNN is presented. Training algorithms of CNFNN in packet mode and on-line are described and discussed. The experimental investigations of CNFNN for market index forecasting at the German stock exchange are carried out. During experiments, the number of cascades, inputs, linguistic terms, and the training-to-test ratio of samples were varied. In the experiments, the optimal values of the aforesaid parameters of the training algorithm were found. The comparative experiments estimating forecasting efficiency of the cascade neo-fuzzy neural network and FNN ANFIS were carried out.

Author Biographies

Yuriy P. Zaychenko, The Educational and Scientific Complex "Institute for Applied System Analysis" of National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv

Yuriy Zaychenko,

Doctor of Science, a professor at the Educational and Scientific Complex "Institute for Applied System Analysis" of National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv.

Galib Hamidov, The Information Technologies Department of Azerbaijanenergo, Baku

Galib Hamidov,

Ph.D., the Director of the Information Technologies Department of Azerbaijanenergo, Baku.

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Published

2017-06-26

Issue

No. 2 (2017)

Section

Problem- and function-oriented computer systems and networks

License

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