Abstract
Architecture of the generalized iterative algorithm GIA GMDH with active neurons is presented based on hybridization of iterative and combinatorial search schemes and the use of interactive technologies. The architecture comprises six standard variants of typical GMDH algorithms. Experiments show that the proposed modifications improve considerably the practical performance of the multilayered GMDH algorithm and accuracy of the simulation results. Solution results are given for modeling Ukraine’s Black Sea economic region GRP as dependent from socio-economic indicators that describe the state region development using generalized iterative algorithm GMDH.
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References
Ivakhnenko, A.G.: Group method of data handling as a rival of the stochastic approximation method. Soviet Autom. Control 3, 58–72 (1968)
Farlow, S.J. (ed.): Self-Organizing Methods in Modeling: GMDH Type Algorithms. Marcel Decker Inc., New York, Basel (1984)
Madala, H.R.: Inductive Learning Algorithms for Complex Systems Modeling. CRC Press Inc., Boca Raton (1994)
Ivakhnenko, A.G., Müller, J.-A.: Recent Developments of Self-Organizing Modeling in Prediction and Analysis of Stock Market. Microelectron. Reliab. 37, 1053–1072 (1997)
Stepashko, V.: Ideas of Academician O.H. Ivakhnenko in the Inductive Modelling Field from Historical Perspective. In: Proceedings of the 4th International Conference on Inductive Modelling ICIM 2013, IRTC ITS NASU, Kyiv, Ukraine, pp. 30–37 (2013)
Stepashko, V.S., Kocherga, Y.L.: Classification and analysis of the noise immunity of external criteria for model selection. Soviet Autom. Control 17(3), 36–47 (1984)
Sheludko, O.I.: GMDH algorithm with orthogonalized complete description for synthesis of models after results of a planned experiment. Soviet Autom. Control 7(5), 46–57 (1974)
Tamura H., Kondo T.: Large-Spatial pattern identification of air pollution by a combined model of source-receptor matrix and revised GMDH. In: Proceedings of IFAC Symposium on Environmental Systems Planning, Design and Control, pp. 373–380. Elsevier, Oxford (1977)
Ivakhnenko, N.A., Marchev, A.A.: Self-organization of a mathematical model for long-term planning of construction and installation works. Soviet Autom. Control 11(3), 10–16 (1978)
Yurachkovskiy, Y.P.: Recovery of polynomial dependences using self-organization. Soviet Autom. Control 14(4), 14–19 (1981)
Parker R.G.J., Tummala M.: Identification of Volterra systems with a polynomial neural network. In: Proceedings of the 1992 International Conference on Acoustics, Speech and Signal Processing ICASSP 1992, San Francisco, vol. 4, pp. 561–564. IEEE (1992)
Hara, K., Yamamoto, T., Terada, K.: Improved dual mode GMDH with automatic switch. Int. J. Syst. Sci. 21(8), 1553–1565 (1990)
Aksyonova, T.I., Volkovich, V.V., Tetko, I.V.: Robust polynomial neural network in quantative-structure activity relationship studies. Syst. Anal. Model. Simul. 43(10), 1331–1341 (2003)
Oh, S.K., Pedrycz, W.: The design of self-organizing polynomial neural networks. Inf. Sci. 141, 237–258 (2002)
Ivakhnenko, A.G., Wunsh, D., Ivakhnenko, G.A.: Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural networks. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1169–1173. IEEE, Piscataway, New Jersey (1999)
Kondo T.: GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification. In: Proceedings of the 37th SICE Annual Conference SICE 1998, pp. 1143–1148. IEEE, Piscataway, New Jersey (1998)
Kordík, P., Náplava, Šnorek, M., Genyk-Berezovskij, P.: The modified GMDH method applied to model complex systems. In: Proceedings of International Conference on Inductive Modelling ICIM 2002, Lviv, Ukraine, pp. 134–138. SRDIII (2002)
Onwubolu, G.C.: Design of hybrid differential evolution and group method of data handling for inductive modeling. In: Proceedings of the IInd International Workshop on Inductive Modelling IWIM 2007, CTU in Prague, Czech Republic, pp. 87–95 (2007)
Stepashko, V., Bulgakova, O., Zosimov, V.: Experimental verification of internal convergence of iterative GMDH algorithms. In: Proceedings of the Vth International Workshop on Inductive Modelling IWIM 2012, IRTC ITS NASU, Kyiv, pp. 53–56 (2012)
Stepashko, V., Bulgakova, O.: Generalized iterative algorithm GIA GMDH. In: Proceedings of the 4th International Conference on Inductive Modelling ICIM 2013, IRTC ITS NASU, Kyiv, pp. 119–123 (2013)
Zosimov, V.V., Bulgakova, O.S., Stepashko, V.S.: Software package for complex systems modelling on the basis of iterative GMDH algorithms with the possibility of network access. Syst. Res. Inf. Technol. 1, 43–55 (2014). (In Ukrainian)
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Stepashko, V., Bulgakova, O., Zosimov, V. (2018). Construction and Research of the Generalized Iterative GMDH Algorithm with Active Neurons. In: Shakhovska, N., Stepashko, V. (eds) Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-70581-1_35
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DOI: https://doi.org/10.1007/978-3-319-70581-1_35
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