Science, Technologies, Innovations №2(22) 2022, 47-51 p


Kliuiev Ye. I. — PhD in Engineering, Associate Professor of the National Aviation University, Cosmonaut Komarov Avenue, 1, Kyiv, Ukraine, 03058; +38 (099)319-72-51;; ORCID: 0000-0001-7572-1142

Zasanska S. V. — PhD in Economics, Associate professor, Head of Department at Ukrainian Institute of Scientific and Technical Expertise and Information, Antonovich Str., 180, 02000; Kyiv, Ukraine; +38 (044) 521-00-10;; ORCID: 0000-0003-3819-0404

Mikhailenko D. O. — Student of the National Aviation University, Cosmonaut Komarov Avenue 1, Kyiv, Ukraine,03058; +38 (099) 627-26-06;; ORCID: 0000-0001-6280-6016

Kliuieva K. Ye. — Postgraduate Student of the National Aviation University, Cosmonaut Komarov Avenue1, Kyiv, Ukraine, 03058; +380 (067) 787-50-20;


Abstract. The article is devoted to determining the affiliation of a certain object (element) to a given set. The main stages of substantiation of the choice of research objects are considered in the article (On), which depend on the goal (for example, applicants for the position, (On – object n)), which meet the requirements of organizations (Ol), (Ol — object l). The matrix apparatus, expert method and means of fuzzy set theory were used in the formation of the list of On objects. Compared with those obtained in the case of generally accepted control algorithms — fuzzy control in some cases gives better results, which has been experimentally proven by many scientific studies. The proposed approach can be used to evaluate the effectiveness of various research objects. Fuzzy management is especially useful when technological processes are too complex to analyze using conventional quantitative methods, or when available sources of information are interpreted at a qualitative level inaccurately or vaguely. To automate the calculations, it is recommended to use software that must match and be written in PHP using the My SQL database.

Keywords: model, object, fuzzy set theory, features, membership function, binary relation, matrix, expert method, efficiency, control algorithm.


  1. Zade, L. A. (1976). Ponyatie lingvisticheskoj peremennoj i ego primenenie k prinyatiyu priblizhennyh reshenij [The concept of a linguistic variable and its application to making approximate decisions]. Moscow. [in Russ.]
  2. Kofman, A. (1982). Vvedenie v teoriyu nechetkih mnozhestv [Introduction to Fuzzy Set Theory]. Moscow, 432 p. [in Russ.]
  3. Klyuev, E. I., & Grinenko, E. A. (2014). Ob odnom podhode ocenki kachestva programmnyh sredstv [On one approach to software quality assessment]. Vіsnik CHerkas’kogo unіversitetu. Serіya Prikladna matematika. Іnformatika,– Bulletin of Cherkasy University. Applied Mathematics Series. Informatics, 38 (331), P. 108-120. [in Russ.]
  4. Yager, R. R.  (Eds.). (1986). Nechetkie mnozhestva i teoriya vozmozhnostej. Poslednie dostizheniya [Fuzzy sets and possibility theory. Latest Achievements]. Moscow, 408 p. [in Russ.]
  5. Deluca, A., & Termini, S. (1968). A definitionofnon-probalistic entropy inthesetting of fuzzyset. Math. Analysis&Appl, 23. P. 421-427.
  6. Klyuev, E. I., Yasenova-Berestyuk, І. S., & Grinenko, O. O. (2014). Laboratornij praktikum [Laboratory workshop]. Kyiv, 48 p. [in Russ.]
  7. Kyryk, V. V. (2019). Matematychnyi aparat shtuchnoho intelektu v elektroenerhetychnykh systemakh: pidruchnyk [Mathematical apparatus of artificial intelligence in power systems: a textbook]. Kyiv, 224 p. [in Ukr.].
  8. Horbatiuk, K. V. (2013). Matematychni modeli v normuvanni pratsi na bazi teorii nechitkykh mnozhyn [Mathematical models in labor rationing based on the theory of fuzzy sets]. Khmelnytskyi, 158 p. [in Ukr.].
  9. Igumnov, B. N. (2000). Kiberneticheskie osnovy postroeniya ekonomicheskih sistem dlya predpriyatij: ucheb. posobie [Cybernetic foundations for building economic systems for enterprises: textbook. allowance]. Hmel’nickii, 344 p. [in Russ.]
  10. Shtovba, S. D. (2007). Proektirovanie nechetkih sistem sredstvami MATLAB [Designing fuzzy systems using MATLAB]. Moscow, 288 p. [in Russ.]
  11. Blyumin, S. L., SHujkova, I. A., Saraev, P. V, & Cherpakov, I. V. (2002). Nechetkaya logika: algebraicheskie osnovy i prilozheniya: monografiya [Fuzzy logic: algebraic foundations and applications: monograph]. Lipeck, 113 p. [in Russ.]