The structural functional model of the information technology prediction of the descent installations design and realization quality

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Holubnyk T. S., Kalynii I. V., Pikh I. V., Senkivskyi V. M. № 2 (51) 13-19 Image Image

The results of the task solutions connected with the research of influence of the expertly determined factors on the process of the book editions assembling descents have been the grounds for distinguishing of the component of structural functional model of information technology forecasting of the installation design and implementation quality. The result highlights the main developed stages of information technology, each of them considered as a complete link with a certain set of processes and (or) information procedures and results obtained by their performance. Based on the generalized description of the examined stages the structural functional information technology forecasting model as the book editions descent installation design and implementation has been developed.

Keywords: fuzzy logic, membership function, linguistic variable factor, quality, information technology, installation descent, phasing, defuzzification, simulation model, structural and functional model.


  • 1. Senkivskyi V. M. and Holubnyk T. S. (2013), Ranking factors influencing the quality of formation installation runs, Printing and publishing, No. 1–2 (61–62), pp. 51–57.
  • 2. Holubnyk T. S. and Senkivskyi V. M. (2014), Synthesis models predicting factors as forming assembly shutter book editions, Printing and publishing, No. 1–2 (65–66), pp. 56–62.
  • 3. Zade L. A. (1976), Concept of linguistic variable and its application to the adoption of approximate solutions, World, Moscow.
  • 4. Zade L. (2001), Role of soft computing and fuzzy logic in understanding the design and development of information systems, intelligent, News of artificial intelligence, No. 2–3, pp. 7–11.
  • 5. Rothstein O. P., Larushkin E. P. and Mityushkin Y. I. (2008), Soft computing in biotechnology: multivariate analysis and diagnostics: monograph, Universam-Vinnytsja, Vinnytsja.
  • 6. Senkivskyi V. M., Pikh I. V. and Holubnyk T. S. (2014), Principles of fuzzy logic in providing quality slopes forming assembly, Scientific Papers [Ukrainian Academy of Printing], No.1–2 (46–47), pp.77–83.
  • 7. Senkivskyi V. M., Pikh I. V., Holubnyk T. S. and Petriv Y. I. (2014), Construction functions of quality factors forming assembly runs, Technology and Printing Technology, No.3 (45), Kyiv Polytechnic Institute, pp. 20–29.
  • 8. Senkivskyi V. M., Pikh I. V. and Holubnyk T. S. (2014), Fuzzy knowledge base and fuzzy logic equation in the implementation of installation runs, Scientific Papers [Ukrainian Academy of Printing], No. 3 (48), pp. 111–119.
  • 9. Syavavko M. S. (2007), Information System Fuzzy expert, Ivan Franko Lviv National University press, Lviv.
  • 10. Shtovba S. D. (2007), Design of fuzzy systems by means of MATLAB, Hotline–Telecom, Moscow.
  • 11. Bartish M. J. and Dudzianyi I. M. (2009), Research operations. Part 3. Decision-making and game theory, Ivan Franko Lviv National University press, Lviv.
  • 12. Zaichenko Y. P. (2006), Operations Research. Tutorial. Seventh edition, revised and updated, Word, Kyiv.
  • 13. Saaty T. (1993), Decision-making (Analytic hierarchy), Radio and communication, Moscow.