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On the Self-organizing Induction-Based Intelligent Modeling

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Advances in Intelligent Systems and Computing III (CSIT 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 871))

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Abstract

The article considers the issues of intellectualization of data-driven means for modeling of complex processes and systems. Some relevant terms of the modeling subject area are analyzed for an adequate explanation of the difference between theory-driven and data-driven approaches. The results are presented of the Internet retrieval for journal and book sources containing the term “intelligent modeling” and its variations in their titles and texts. Analysis of these sources made it possible to suggest an advanced conception of the intelligent modeling. It introduces three main levels of intellectualization of such means: offline intelligent modeling for constructing models of objects from available data; online intelligent modeling in an operating system of control or decision making; comprehensive intelligent modeling of work modes of a complex system. The original features of GMDH-based self-organizing inductive modeling are characterized showing that GMDH is one of the most powerful methods of data mining and computational intelligence for tasks being solved under conditions of uncertain and incomplete prior information. The inductive modeling algorithms can be the reasonable basis for creating advanced intelligent modeling tools.

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References

  1. Ljung, L.: System Identification: Theory for the User. Prentice-Hall, Englewood Cliffs (1999)

    Book  Google Scholar 

  2. Manhart, K.: Artificial intelligence modelling: data driven and theory driven approaches. In: Troitzsch, K.G., Mueller, U., Gilbert, G.N., Doran, J.E. (eds.) Social Science Micromodeling, pp. 416–431. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  3. Burov, Y.V.: A system of modeling of the intellectual network of business processes. In: Information Systems and Networks, Bulletin of the Lviv National Polytechnic University, vol. 610, pp. 34–39 (2008). (In Ukrainian)

    Google Scholar 

  4. Lytvyn, V.V.: Modeling of intelligent decision support systems using the ontological approach. Radioelektron. Inform. Control. 2(25), 93–101 (2011). (In Ukrainian)

    Google Scholar 

  5. Zachko, O.B.: Intelligent modeling of product parameters of the infrastructure project (on the example of Lviv airport). East. Eur. J. Adv. Technol. 10(1), 92–94 (2013). (In Ukrainian)

    Google Scholar 

  6. Korolev, O., Krulikovsky, A.P.: Intelligent methods for modeling of project control processes, Scientific notes of the V.I. Vernadsky Taurida National University, Economics and management series, vol. 26(1), pp. 73–86 (2013). (In Ukrainian)

    Google Scholar 

  7. Timashova, L.A., Vitkovski, T.: Technology of Intellectual Manufacturing Modeling of Virtual Enterprises. Informatics and modeling. Bulletin of the National Technical University “Kharkov Polytechnic Institute”, vol. 32, pp. 136–147 (2015). (In Russian)

    Google Scholar 

  8. Valkman, Yu.R., Stepashko, P.V.: On the way to constructing the ontology of intelligent modeling. In: Inductive Modeling of Complex Systems, vol. 7, pp. 101–115. IRTC ITS NASU, Kyiv (2015). (In Russian)

    Google Scholar 

  9. Merkuriev, YuA, Teilans, A.A., Merkuryeva, G.V.: Intelligent modeling of production processes. Softw. Prod. Syst. 3, 43–49 (1991). (In Russian)

    Google Scholar 

  10. Gladkiy, S.L., Stepanov, N.A., Yasnitsky, L.N.: Intelligent Modeling of Physical Problems. Institute of Computer Studies, Moscow (2006). (In Russian)

    Google Scholar 

  11. Mikoni, S.V., Kiselev, I.S.: Intelligent modeling of expert preferences on matrices of paired comparisons. In: Collected papers of Vseros Conference on “Simulation Modeling. Theory and Practice” SIMMOD-2007, CSRIETS, SpB, vol. 1, pp. 182–186 (2007). (In Russian)

    Google Scholar 

  12. Novikova, E., Demidov, N.: Means of intelligent analysis and modeling of complex processes as a key tool of situational control. World Inf. Technol. 3, 84–89 (2012). (In Russian)

    Google Scholar 

  13. Gorbatkov, S.A., Rashitova, O.B., Solntsev, A.M.: Intelligent modeling in the problem of decision making within the tax administration. Bulletin Ufa State Aviation Technical University, vol. 1(17), pp. 182–187 (2013). (In Russian)

    Google Scholar 

  14. Polupanov, D.V., Khayrullina, N.A.: Intelligent modeling of segmentation of shopping centers on the basis of Kohonen self-organizing maps. Internet J. Naukovedenie, 1, 1–15 (2014). (In Russian)

    Google Scholar 

  15. Glushkov, S.V., Levchenko, N.G.: Intelligent modeling as a tool to improve the management of the transport and logistics process. In: Proceedings of the International Scient.-tekhn. Conference of the Eurasian Scientific Association, pp. 1–5 (2014). (In Russian)

    Google Scholar 

  16. Amarger, R., Biegler, J.L.T., Grossmann, I.E.: An Intelligent Modelling Interface for Design Optimization. Carnegie Mellon University, Pittsburgh (1990)

    Google Scholar 

  17. Bille, W., Pellens, B., Kleinermann, F., De Troyer, O.: Intelligent modelling of virtual worlds using domain ontologies. In: Proceedings of the Workshop of Intelligent Computing (WIC) held in Conjunction with the MICAI 2004 Conference, Mexico City, pp. 272–279 (2004)

    Google Scholar 

  18. Balic, J., Cus, F.: Intelligent modelling in manufacturing. J. Achiev. Mater. Manuf. Eng. 24(1), 340–349 (2007)

    Google Scholar 

  19. Al-Shareef, A.J., Abbod, M.F.: Intelligent modelling techniques of power load forecasting for the western area of Saudi Arabia. J. King Abdulaziz Univ. Eng. Sci 21(1), 3–18 (2010)

    Article  Google Scholar 

  20. Ćojbašić, Ž.M., et al.: Computationally intelligent modelling and control of fluidized bed combustion process. Therm. Sci. 15(2), 321–338 (2011)

    Article  Google Scholar 

  21. Kołodziej, J., Khan, S.U., Burczyński, T. (eds.) Advances in Intelligent Modelling and Simulation: Artificial Intelligence-Based Models and Techniques in Scalable Computing. Springer, Heidelberg (2012)

    Google Scholar 

  22. Sharma, A., Yadava, V., Judal, K.B.: Intelligent modelling and multi-objective optimisation of laser beam cutting of nickel based superalloy sheet. Int. J. Manuf. Mater. Mech. Eng. (IJMMME) 3(2), 1–16 (2013)

    Article  Google Scholar 

  23. Simjanoska, M., Gusev, M., Madevska-Bogdanova, A.: Intelligent modelling for predicting students’ final grades. In: Proceedings of 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, pp. 1216–1221. IEEE Publisher (2014)

    Google Scholar 

  24. Ivakhnenko, A.G.: The group method of data handling – a rival of the method of stochastic approximation. Sov. Autom. Control. 1(3), 43–55 (1968)

    Google Scholar 

  25. Madala, H.R., Ivakhnenko, A.G.: Inductive Learning Algorithms for Complex Systems Modeling. CRC Press, New York (1994)

    MATH  Google Scholar 

  26. Stepashko, V.: Developments and prospects of GMDH-based inductive modeling. In: Shakhovska, N., Stepashko, V. (eds.) Advances in Intelligent Systems and Computing II. CSIT 2017. AISC Series, vol. 689, pp. 474–491. Springer, Cham (2018)

    Google Scholar 

  27. https://en.wikipedia.org/wiki/Deep_learning

  28. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  29. Zaychenko, Y.: The investigations of fuzzy group method of data handling with fuzzy inputs in the problem of forecasting in financial sphere. In: Proceedings of the II International Conference on Inductive Modelling ICIM-2008, IRTC ITS NASU, Kyiv, pp. 129–133 (2008)

    Google Scholar 

  30. Zgurogvsky, M., Zaychenko, Yu.: The fundamentals of computational intelligence: System approach. Springer, Cham (2016)

    Google Scholar 

  31. Huang, W., Oh, S.K., Pedrycz, W.: Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling. IEEE Trans. Fuzzy Syst. 10(5), 607–621 (2002)

    Article  Google Scholar 

  32. Oh, S.K., Park, B.J.: Self-organizing neuro-fuzzy networks in modeling software data. Neurocomputing 64, 397–431 (2005)

    Article  Google Scholar 

  33. Bodyanskiy, Y., Vynokurova, O., Dolotov, A., Kharchenko, O.: Wavelet-neuro-fuzzy network structure optimization using GMDH for the solving forecasting tasks. In: Proceedings of the 4th International Conference on Inductive Modeling ICIM 2013, Kyiv, pp. 61–67 (2013)

    Google Scholar 

  34. Bodyanskiy, Y.V., Vynokurova, O.A., Dolotov, A.I.: Self-learning cascade spiking neural network for fuzzy clustering based on Group Method of Data Handling. J. Autom. Inf. Sci. 45(3), 23–33 (2013)

    Article  Google Scholar 

  35. Voss, M.S., Feng, X.: A new methodology for emergent system identification using particle swarm optimization (PSO) and the group method of data handling (GMDH). In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1227–1232. Morgan Kaufmann Publishers, New York (2002)

    Google Scholar 

  36. Jirina, M., Jirina, Jr., M.: Genetic selection and cloning in GMDH MIA method. In: Proceedings of the II International Workshop on Inductive Modelling, IWIM 2007, pp. 165–171. CTU, Prague (2007)

    Google Scholar 

  37. Lytvynenko, V.: Hybrid GMDH cooperative immune network for time series forecasting. In: Proceedings of the 4th International Conference on Inductive Modelling, pp. 179–187. IRTC ITS NASU, Kyiv (2013)

    Google Scholar 

  38. Moroz, O., Stepashko, V.: On the approaches to construction of hybrid GMDH algorithms. In: Proceedings of 6th International Workshop on Inductive Modelling IWIM-2013, pp. 26–30. IRTC ITS NASU, Kyiv (2015). ISBN 978-966-02-7648-2

    Google Scholar 

  39. Moroz, O., Stepashko, V.: Hybrid sorting-out algorithm COMBI-GA with evolutionary growth of model complexity. In: Shakhovska, N., Stepashko, V. (eds.) Advances in Intelligent Systems and Computing II. AISC series, vol. 689, pp. 346–360. Springer, Cham (2017)

    Google Scholar 

  40. Ivakhnenko, A.G., Ivakhnenko, G.A., Mueller, J.-A.: Self-organization of neuronets with active neurons. Pattern Recognit. Image Anal. 4(4), 177–188 (1994)

    Google Scholar 

  41. 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)

    Google Scholar 

  42. Muller, J.-A., Lemke, F.: Self-Organizing Data Mining. An Intelligent Approach to Extract Knowledge from Data. Springer, Heidelberg (1999)

    Google Scholar 

  43. Tyryshkin, A.V., Andrakhanov, A.A., Orlov, A.A.: GMDH-based modified polynomial neural network algorithm. In: Onwubolu, G. (ed.) Book GMDH-Methodology and Implementation in C (With CD-ROM), Chap. 6, pp. 107–155. Imperial College Press, London (2015)

    Chapter  Google Scholar 

  44. Stepashko, V., Bulgakova, O., Zosimov, V.: 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. AISC series, vol. 689, pp. 474–491. Springer, Cham (2018)

    Google Scholar 

  45. Hancock, P.A., Chignell, M.H. (eds.) Intelligent Interfaces Theory, Research, and Design. North Holland, New York (1989)

    Google Scholar 

  46. Kolski, C., Le Strugeon, E.: A review of “intelligent” human-machine interfaces in the light of the ARCH model. Int. J. Hum. Comput. Interact. 10(3), 193–231 (1998)

    Article  Google Scholar 

  47. Rogers, Y., Sharp, H., Preece, J.: Interaction Design: Beyond Human-Computer Interaction, 3rd edn. Wiley, Chichester (2011)

    Google Scholar 

  48. http://iui.acm.org/2018/. Last Accessed 22 July 2018

  49. https://en.wikipedia.org/wiki/Intelligent_user_interface. Last Accessed 29 July 2018

  50. https://web.cs.wpi.edu/Research/airg/IntInt/intint-outline.html. Last Accessed 29 July 2018

  51. Pidnebesna, H., Stepashko, V.: On construction of inductive modeling ontology as a metamodel of the subject field. In: Proceedings of the International Conference on Advanced Computer Information Technologies, University of South Bohemia, Ceske Budejovice, pp. 71–74 (2018)

    Google Scholar 

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Authors and Affiliations

  1. International Research and Training Centre for Information Technologies and Systems of the NAS and MES of Ukraine, Kyiv, 03680, Ukraine

    Volodymyr Stepashko

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  1. Volodymyr Stepashko
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Correspondence to Volodymyr Stepashko .

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  1. Lviv Polytechnic National University, Lviv, Ukraine

    Natalia Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

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Stepashko, V. (2019). On the Self-organizing Induction-Based Intelligent Modeling. In: Shakhovska, N., Medykovskyy, M. (eds) Advances in Intelligent Systems and Computing III. CSIT 2018. Advances in Intelligent Systems and Computing, vol 871. Springer, Cham. https://doi.org/10.1007/978-3-030-01069-0_31

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