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UDC 303.444
A. Kupchyn1, V. Komarov2, I. Borokhvostov3, A. Kuprinenko4,
V. Sotnyk5, M. Bilokur6, V. Oleksiiuk7



1 Central Scientific Research Institute of Armament
and Military Equipment of the Armed Forces of Ukraine,
Kyiv, Ukraine

kupchyn.artem@ukr.net

2 Central Scientific and Research Institute
of the Armed Forces of Ukraine, Kyiv, Ukraine

komarvlad@ukr.net

3 Central Scientific Research Institute of Armament
and Military Equipment of the Armed Forces of Ukraine,
Kyiv, Ukraine

borohvostov@icloud.com

4 Hetman Petro Sahaidachnyi National Ground Forces
Academy, Lviv, Ukraine

Kyprinenko@ukr.net

5 Central Scientific Research Institute of Armament
and Military Equipment of the Armed Forces of Ukraine,
Kyiv, Ukraine

Sotvladislav@gmail.com

6 Central Scientific Research Institute of Armament
and Military Equipment of the Armed Forces of Ukraine,
Kyiv, Ukraine

nikolas200578@gmail.com

7 Central Scientific and Research Institute
of the Armed Forces of Ukraine, Kyiv, Ukraine

voleksiyk@ukr.net

TECHNOLOGY FORESIGHT MODEL BASED ON FUZZY LOGIC

Abstract. The developed technology foresight model allows eliminating the human from decision-making. The criticality limits of technologies are not determined by an expert, but are calculated on the basis of the proposed equidistant points. The paper shows the influence of different membership functions on the criticality assessment. A comparison with the existing method of technology foresight is made.

Keywords: critical technologies, fuzzy logic, membership function determination, technology foresight.


FULL TEXT

REFERENCES

  1. Sotnik V.V., Kupchin A.V. The development of critical technologies is an important step into the future of Ukraine. Nauka ta naukoznavstvo. 2020. N 1 (107). P. 34–48. https://doi.org/10.15407/ sofs2020.01.034.

  2. Slyusar V.I., Sotnik V.V., Kupchin A.V., Shostak V.G. Breakthrough technologies in the defense sphere of Ukraine. Ozbroyennya ta viysʹkova tekhnika. 2020. N 4 (28). P. 13–23.

  3. Calof J., Meissner D., Vishnevskiy K. Corporate foresight for strategic innovation management: the case of a Russian service company. Foresight. 2020. Vol. 22, N 1. P. 14–36. https://doi.org/10.1108/ FS-02-2019-0011,

  4. Gavigan J.P., Scapolo F. A comparison of national foresight exercises. Foresight. 1999. Vol. 1, N 6. P. 495–517. https://doi.org/10.1108/14636689910802368.

  5. Wonglimpiyarat J. Technology foresight: creating the future of Thailand’s industries. Foresight. 2006. Vol. 8, N 4. P. 23–33. https://doi.org/10.1108/14636680610682012.

  6. Kovarikova L., Grosova S., Baran D. Critical factors impacting the adoption of foresight by companies. Foresight. 2017. Vol. 19, N 6. P. 541–558. https://doi.org/10.1108/FS-02-2017-0009.

  7. Omrane H., Masmoudi M.S., Masmoudi M. Fuzzy logic based control for autonomous mobile robot navigation. Computational Intelligence and Neuroscience. 2016. Vol. 2016. Article ID 9548482. P. 1–10. http://doi.org/10.1155/2016/9548482.

  8. Jaafari A., Zenner E.K., Panahi M., Shahabi H. Hybrid artificial intelligence models based on a neuro-fuzzy system and met heuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and Forest Meteorology. 2019. Vol. 266–267. P. 198–207. https://doi.org/ 10.1016/j.agrformet.2018.12.015.

  9. Govinda K., Singlaand K., Jain K. Fuzzy based uncertainty modeling of Cancer Diagnosis System. 2017. Proc. 2017 International Conference on Intelligent Sustainable Systems (ICISS) (7–8 Dec. 2017, Palladam, India). Palladam, 2017. P. 740–743, https://doi.org/10.1109/ISS1.2017.8389272.

  10. Paladchenko O.F., Molchanova I.V. Modern approaches and methods of forecasting research: world experience and the possibility of its use in Ukraine. Nauka, tekhnolohiyi, innovatsiyi. 2018. N 2 (6). P. 23–32.

  11. Gorbulin V.P., Shekhovtsov V.S., Shevtsov A.I. Problematic issues of identification and implementation of critical technologies in the field of armaments production. Visnyk NAN Ukrayiny. 2018. N 2. P. 3–9.

  12. Romanowski M., Nadolny K. Technological foresight — characterisation of research methods used in prospective analysis. Journal of Mechanical and Energy Engineering. 2018. Vol. 2, N 2. P. 101–108. https://doi.org/10.30464/jmee.2018.2.2.101.

  13. Gibson E., Daim T., Garces E., Dabic M. Technology foresight: a bibliometric analysis to identify leading and emerging methods. Foresight and STI Governance. 2018. Vol. 12, N 1. P. 6–24. https://doi.org/10.17323/2500-2597.2018.1.6.24.

  14. Bhring J., Liedtka J. Embracing systematic futures thinking at the intersection of Strategic Planning, Foresight and Design. Journal of Innovation Management. 2018. Vol. 6, N 3. P. 134–152. https://doi.org/10.24840/2183-0606 sub 006-003 sub 0006.

  15. Dovgopoliy A.S., Sotnik V.V., Tomchuk V.V. et al. Priority development of critical technologies is a guarantee of strengthening the state's defense capabilities and economic growth. Ozbroyennya ta viysʹkova tekhnika. 2019. № 1 (21). С. 15–21. https://doi.org/10.34169/2414-0651.2019.1(21).15-21.

  16. Kupchyn A., Sotnyk V. Critical technologies in the defense sphere. A new look. Ozbroyennya ta viysʹkova tekhnika. 2019. Vol. 22, N 2. P. 35–41. https://doi.org/10.34169/2414-0651.2019. 2(22).35-41.

  17. Aengchuan P., Phruksaphanrat B. Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control. Journal of Intelligent Manufacturing. 2018. Vol. 29, Iss. 4. P. 905–923. https://doi.org/10.1007/s10845-015-1146-1.

  18. Erturk E., Sezer E.A. Software fault prediction using Mamdani type fuzzy inference system. International Journal of Data Analysis Techniques and Strategies. 2016. Vol. 8, N 1. P. 14–28. https://doi.org/10.1504/ijdats.2016.075971.

  19. Hussain H.I., Slusarczyk B., Kamarudin F., Thaker H.M.T., SzczepaK. An investigation of an adaptive neuro-fuzzy inference system to predict the relationship among energy intensity, globalization, and financial development in major ASEAN economies. Energies. 2020. Vol. 13, Iss. 4. P. 1–17. https://doi.org/10.3390/en13040850.

  20. Garcia J.S., Slvarez C.A.A., Gomez J.M.C., Toro J.J.A. Measuring organizational capabilities for technological innovation through a fuzzy inference system. Technology in Society. 2017. Vol. 50. P. 93–109. https://doi.org/10.1016/j.techsoc.2017.05.005.

  21. Sotnik V.V., Rasstrigin O.O., Kupchin A.V. Methods of selection of critical technologies. Suchasni informatsiyni tekhnolohiyi u sferi bezpeky ta oborony. 2020. Vol. 37, N 1. P. 67–76. http://doi.org/ 10.33099/2311-7249/2020-37-1-67-76.

  22. Shtovba S.D. Designing fuzzy systems using MATLAB [in Russian]. Moscow: Goryachaya liniya-Telecom, 2007. 288 p.

  23. Leonenkov A.V. Fuzzy modeling in MATLAB and fuzzyTECH [in Russian]. SPb .: BHV-Petersburg, 2005. 736 p.

  24. Alekseeva I.V., Gaidey V.O., Dykhovychny O.O., Fedorova L.B. Mathematics at the Technical University [in Ukrainian]. Vol. 2. Kyiv: Ed. house "Condor”, 2019. 504 p.

  25. M.K., Jurkin E. Equidistant sets of conic and line. Proc. 18th International Conference on Geometry and Graphics (3–7 August 2018, Milan, Italy). Milan, 2018. P. 277–289. https://doi.org/ 10.1007/978-3-319-95588-9 sub 22.

  26. Slyusar V. et al. Method for determining membership function based on equidistant points. Proc. International conference of specialized and multidisciplinary scientific researches (11 December 2020, Amsterdam, the Netherlands). Amsterdam, 2020. Vol. 2. P. 27–30. http://doi.org/10.36074/ 11.12.2020.v2.07.




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