Hybrid GMDH deep learning networks – analysis, optimization and applications in forecasting at financial sphere

Authors

  • Yuriy Zaychenko Educational and Scientific Complex "Institute for Applied System Analysis" of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine https://orcid.org/0000-0001-9662-3269
  • Helen Zaychenko Educational and Scientific Complex "Institute for Applied System Analysis" of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine https://orcid.org/0000-0002-4546-0428
  • Galib Hamidov Azerishiq, Baku, Azerbaijan

DOI:

https://doi.org/10.20535/SRIT.2308-8893.2022.1.06

Keywords:

hybrid deep learning networks, self-organization, parameters and structure optimization, forecasting

Abstract

In this paper, the new class of deep learning (DL) neural networks is considered and investigated — so-called hybrid DL networks based on self-organization method Group Method of Data Handling (GDMH). The application of GMDH enables not only to train neural weights, but also to construct the network structure as well. Different elementary neurons with two inputs may be used as nodes of this structure. So the advantage of such a structure is the small number of tuning parameters. In this paper, the optimization of parameters and the structure of hybrid neo-fuzzy networks was performed. The application of hybrid Dl networks for forecasting market indices was considered with various forecasting intervals: one day, one week, and one month. The experimental investigations of hybrid GMDH neo-fuzzy networks were carried out and comparison of its efficiency with FNN ANFIS in the forecasting problem was performed which enabled to estimate their efficiency and advantages.

Author Biographies

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

Yuriy P. Zaychenko,

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

Helen Zaychenko, Educational and Scientific Complex "Institute for Applied System Analysis" of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv

Helen Yu. Zaychenko,

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

Galib Hamidov, Azerishiq, Baku

Galib Hamidov,

Ph.D., the head of the Information Technologies Department of Azerishiq, Baku, Azerbaijan.

References

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.

G. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets”, Neural Computation, vol. 18, no. 7, pp. 1527–1554, May 2006.

Y. Bengio, Y. LeCun, and G. Hinton, “Deep learning”, Nature, no. 521, pp. 436–444, May 2015.

J. Schmidhuber, “Deep learning in neural networks: an overview”, Neural Networks, no. 61, pp. 85–117, 2015.

A.G. Ivakhnenko, G.A. Ivakhnenko, and J.A. Mueller, “Self-organization of the neural networks with active neurons”, Pattern Recognition and Image Analysis, 4, 2, pp. 177–188, 1994.

A.G. Ivakhnenko, D. Wuensch, and G.A. Ivakhnenko, “Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural networks”, Neural Networks, 2, pp. 1169–1173, 1999.

G.A. Ivakhnenko, “Self-organization of neuronet with active neurons for effects of nuclear test explosions forecasting”, System Analysis Modeling Simulation, 20, pp. 107–116, 1995.

M. Zgurovsky and Yu. Zaychenko, Fundamentals of computational intelligence: System approach. Springer, 2016.

L. X. Wang and J.M. Mendel, “Fuzzy basis functions, universal approximation, and orthogonal least-squares learning”, IEEE Trans. on Neural Networks, vol. 3, no. 5, pp. 807–814, 1992.

J. S. Jang, “ANFIS: Adaptive-network-based fuzzy inference systems”, IEEE Trans. on Systems, Man, and Cybernetics, 23, pp. 665–685, 1993.

T. Yamakawa, E. Uchino, T. Miki, and H. Kusanagi, “A neo-fuzzy neuron and its applications to system identification and prediction of the system behavior”, in Proc. 2nd Intern. Conf. Fuzzy Logic and Neural Networks "LIZUKA-92", Lizuka, 1992, pp. 477–483.

Ye. Bodyanskiy, N. Teslenko, and P. Grimm, “Hybrid evolving neural network using kernel activation functions”, in Proc. 17th Zittau East-West Fuzzy Colloquium, Zittau/Goerlitz, HS, 2010, pp. 39–46.

Ye. Bodyanskiy, Yu. Zaychenko, E. Pavlikovskaya, M. Samarina, and Ye. Viktorov, “The neo-fuzzy neural network structure optimization using the GMDH for the solving forecasting and classification problems”, Proc. Int. Workshop on Inductive Modeling, Krynica, Poland, 2009, pp. 77–89.

Ye. Bodyanskiy, O. Vynokurova, A. Dolotov, and O. Kharchenko, “Wavelet-neuro-fuzzy network structure optimization using GMDH for the solving forecasting tasks”, in Proc. 4th Int. Conf. on Inductive Modelling ICIM 2013, Kyiv, 2013, pp. 61–67.

Ye. Bodyanskiy, O. Vynokurova, and N. Teslenko, “Cascade GMDH-wavelet-neuro-fuzzy network”, in Proc. 4th Int. Workshop on Inductive Modeling “IWIM 2011”, Kyiv, Ukraine, 2011, pp. 22–30.

Ye. Bodyanskiy, O. Boiko Yu. Zaychenko, and G. Hamidov, “Evolving Hybrid GMDH-Neuro-Fuzzy Network and Its Applications”, in Proceedings of the International conference SAIC 2018, Kiev, Ukraine, 2018.

Evgeniy Bodyanskiy, Yuriy Zaychenko, Olena Boiko, Galib Hamidov, and Anna Zelikman, “The hybrid GMDH-neo-fuzzy neural network in forecasting problems in financial sphere”, in Proceedings of the International conference IEEE SAIC 2020, Kiev, Ukraine, 2020.

T. Ohtani, “Automatic variable selection in RBF network and its application to neuro-fuzzy GMDH”, Proc. Fourth Int. Conf. on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000, vol. 2, pp. 840–843.

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Published

2022-04-25

Issue

Section

Theoretical and applied problems of intellectual systems for decision making support