Control Systems and Computers

https://doi.org/10.15407/csc.2022.02.033

Control Systems and Computers, 2022, Issue 2 (298), pp. 33-42

UDC 6:004.8

O.H. Moroz, PhD, senior research scientist, Department for Technologies of Inductive Modelling, International Research and Training Center for Information Technologies and Systems of the NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine, ORCID: https://orcid.org/0000-0002-0356-8780,  olhahryhmoroz@gmail.com, 

Ya.M. Linder, PhD, senior research scientist, Department of Human-Machine Systems, Senior researcher, International Research and Training Centre of Information Technologies and Systems of the NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine, ORCID: https://orcid.org/0000-0003-1076-9211, yaroslav.linder@gmail.com, 

Problem of Сonstructing the GMDH Neural Networks with Active Neurons

Characteristics of the existing neural networks of GMDH with active neurons are given and their main advantages and disadvantages are analyzed. Two approaches of increasing efficiency of inductive construction of complex system models from statistical data based on a new hybrid GMDH neural networks with active neurons using methods of computational intelligence are proposed. Effectiveness of these networks are compared with classical approaches on artificial inductive modelling tasks (noisy linear and nonlinear models).

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Keywords: inductive modeling, GMDH neural network, active neurons, computational intelligence, genetic algorithms.

1. Ivakhnenko, A.G., 1968. “Group method of data handling as a rival of the stochastic approximation method”. Soviet Automatic Control. 3, pp. 58-72.
2. Stepashko, V., 2013. “Ideas of Academician O.H. Ivakhnenko in the Inductive Modelling Field from Historical Perspective”. Proc. of the 4th Int. Conf. on Inductive Modelling ICIM-2013, 2013. IRTC ITS NASU, Kyiv, Ukraine. pp. 30-37.
3. Stepashko, V., 2018. “Developments and Prospects of GMDH-Based Inductive Modeling”. Advances in Intelligent Systems and Computing II: Selected Papers from the International Conference on Computer Science and Information Technologies CSIT 2017. AISC book series, Springer..Vol. 689, pp. 474-491.
   https://doi.org/10.1007/978-3-319-70581-1_34
4. Stepashko, V., 2019. “On the Self-Organizing Induction-Based Intelligent Modeling”. Advances in Intelligent Systems and Computing III: Selected Papers from the International Conference on Com-puter Science and Information Technologies. CSIT 2018. AISC book series, Springer. Vol. 871, pp. 433-448.
   https://doi.org/10.1007/978-3-030-01069-0_31
5. Madala, H.R., 1994. Inductive Learning Algorithms for Complex Systems Modeling. Boca Raton: CRC Press Inc.
6. Stepashko, V.S., 1981. “Combinatorial Algorithm of the Group Method of Data Handling with Optimal Model Scanning Scheme”. Soviet Automatic Control, 14 (3), pp. 24-28.
7. Moroz, O.H., Stepashko, V.S., 2016. “Comparative analysis of generators of model structures in interpolated algorithms of GMDH”. Inductive modeling of complex systems: Coll. sciences works, K .: IRTC ITS NASU, 8. pp. 133-148. (In Ukrainian).
8. Moroz, O.H., Stepashko, V.S., 2021. The sorting algorithms of inductive modeling with genetic oper-ators. Kyiv, Osvita Ukrayiny. 216 p. (In Ukrainian).
9. Moroz, O., 2021. “Analysis of MIA GMDH as a self-organizing deep neural network”. Proc. of the IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT). Lviv: LNPU, V.1, pp. 394-397.
   https://doi.org/10.1109/CSIT52700.2021.9648603
10. Moroz, O.H., Stepashko, V.S., 2021. “Comparative features of MIA GMDH and deep feed-forward neural networks”. Cybernetics and computer engineering, 4 (206), pp. 5-20.
    https://doi.org/10.15407/kvt206.04.005
11. Stepashko, V.S., Bulgakova, O.S., Zosimov, V.V., 2010. “Hybrid algorithms for self-organization of models for predicting complex processes”. Inductive modeling of complex systems. Collected research papers, Issue 2, Kyiv: IRTC ITS NASU, pp. 236-246 (In Ukrainian).
12. Stepashko, V., Bulgakova, O., Zosimov, V., 2012. “Experimental Verification of Internal Conver-gence of Iterative GMDH Algorithms”. Proceedings of the V International Workshop on Inductive Modelling IWIM-2012, IRTC ITS NASU, Kyiv, pp. 53-56.
13. Stepashko, V.S., Bulgakova, A.S., 2013. “Generalized iterative algorithm of the method of group accounting of arguments”. Upravlyayushchiye sistemy i mashiny. No 2, pp. 5-17. (In Russian).
14. Stepashko, V., Bulgakova, O., 2013. “Generalized Iterative Algorithm GIA GMDH”. Proceedings of the 4th International Conference on Inductive Modelling ICIM-2013. IRTC ITS NASU, Kyiv.2013. P. 119-123.
15. Zosimov, V.V., Bulgakova, O.S., Stepashko, V.S., 2014. “Software package for complex systems modelling on the basis of iterative GMDH algorithms with the possibility of network access”. System Research and Information Technologies, 1, pp. 43-55. (In Ukrainian)
16. Stepashko, V., Bulgakova, O., Zosimov, V., 2017. “Construction and Research of the Generalized Iterative GMDH Algorithm with Active Neurons”. Advances in Intelligent Systems and Computing II. AISC book series. Cham: Springer, pp. 492-510.
    https://doi.org/10.1007/978-3-319-70581-1_35
17. Stepashko, V.S., Bulgakova, O.S., Zosimov, V.V., 2018. Iterative algorithms for inductive modeling. Kyiv: Naukova dumka, 190 p. (In Ukrainian).
18. Moroz, O.H., Stepashko, V.S., 2015. “An overview of hybrid structures of GMDH-like neural net-works and genetic algorithms”. Inductive modeling of complex systems: Coll. sciences works. No 7, pp. 173-191 (In Ukrainian)
19. Hollannd, J.H., 1975. Adaptation in natural and artificial systems. University of Michigan, 210 p.
20. Goldberg, D.E., 1989. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, 432p.
21. Hlybovets, M.M., Gulaeva, N.M., 2013. Evolutionary algorithms. K.: NaUKMA, 828p. (In Ukraini-an)
22. Reeves, C.R., Rowe, J.E., 2003. Genetic algorithms: principles and perspectives. A Guide to GA Theory. Kluwer Academic Publishers, 332 p.
    https://doi.org/10.1007/b101880
23. Ivakhnenko, A.G., Wunsh, D., Ivakhnenko, G.A., 1999. “Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural networks”. Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway, New Jersey, pp. 1169-1173.
24. Ivakhnenko, A.G., Ivakhnenko, G.A., Mueller, J.A., 1995. “Self-organization of neuronets with Active Neurons”. Pattern Recognition and Image Analysis, 4 (4), pp. 177-188.
25. Muller, J.-A., Lemke, F., 1999. “Self-organizing for active neurons of neural networks”. Proceed-ings of the International Joint Conference on Neural Networks, IEEE, Piscataway, New Jersey, pp. 1169-1173.
26. Ivakhnenko, G.A. Model-Free Analogues as Active Neurons for Neural Network Self-organization. [Online]. Available at: <http://www.gmdh.net/articles/algor/analogue.pdf> [Accessed 17 Sep. 2021].
27. Lemke, F., 2008. “Parallel Self-Organizing Modeling”. Proc. of the 2nd Int. Conf. on Inductive Mod-elling ICIM-2008. K.: IRTC ITS NANU, pp. 176-184.
28. Oh, S.K., Pedrycz, W., 2002. “The Design of Self-Organizing Polynomial Neural Networks”. Inf. Sci., 141, pp. 237-258.
    https://doi.org/10.1016/S0020-0255(02)00175-5
29. Kondo, T., 1998. “GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification”. Proceedings of the 37th SICE Annual Conference SICE’98, pp.1143-1148. IEEE, Piscataway.
30. Kordik, P., Naplava, P., Snorek, M., Genyk-Berezovskij, P., 2002. “The Modified GMDH Method Applied to Model Complex Systems”. Proceedings of International Conference on Inductive Model-ling ICIM’2002. SRDIII, Lviv, Ukraine, pp.134-138.
31. Kordik, P., 2006. Fully automated knowledge extraction using group of adaptive model evolution: PhD thesis. Electrical Engineering and Information Technology. Prague: CTU, 150 p.
32. Onwubolu, G.C., 2007. “Design of Hybrid Differential Evolution and Group Method of Data Han-dling for Inductive Modeling”. Proceedings of the II International Workshop on Inductive Modelling IWIM-2007, CTU in Prague, Czech Republic, pp. 87-95.

Received 31. 07.2022