Application of algorithmic procedures of imprecise inference based on fuzzy knowledge to the solution of applied problems
https://doi.org/10.21822/2073-6185-2025-52-4-73-82
Abstract
Objective. This article explores the application of fuzzy logic to practical technological problems that require evaluating alternatives and then making a choice. The goal is to develop a methodology for applying a generalized approach to fuzzy inference rules to solving diverse, unrelated problems.
Method. The application of algorithmic procedures to a specific range of problems is considered.
Result. The following processes are considered: systematic monitoring of learning outcomes with recommendations for modifying learning paths; employee selection upon hiring and identifying professional suitability; determining the quality of finished products at an industrial enterprise to improve competitiveness, etc. This approach makes it possible to apply the same algorithm to describe linguistic uncertainty in various research areas, relying on the use of linguistic variables. Models and methods of fuzzy set theory are tested, and specific tools for describing objects that do not have clear and unambiguous boundaries are presented.
Conclusion. The universality of the applied mathematical apparatus for solving multidisciplinary problems is proven.
About the Authors
N. V. DatsenkoRussian Federation
Natalia V. Datsenko - Сand. Sci. (Eng.), Assoc. Prof., Assoc. Prof., Department of Information Technologies, Modeling and Management.
9 Revolution Ave., Voronezh 394036
L. A. Korobova
Russian Federation
Lyudmila A. Korobova - Сand. Sci. (Eng.), Assoc. Prof., Assoc. Prof., Department of Information Technologies, Modeling and Management.
9 Revolution Ave., Voronezh 394036
I. A. Matytsina
Russian Federation
Irina A. Matytsina - Сand. Sci. (Eng.), Assoc. Prof., Department of Mathematics, information systems and technologies.
174, l Leninsky Ave., Voronezh 394033
References
1. Bolotova, L.S. Decision support systems in 2 parts. Part 2: textbook and practical course for universities / L.S. Bolotova; editors-in-chief V. N. Volkova, E. S. Bolotov. Moscow: Yurait Publishing House, 2023. - 250 p. (Higher education). ISBN 978-5-9916-8251-0. — Text: electronic // Educational platform Urayt — URL: https://urait.ru/bcode/513142 (date of access: 14.05.2025)
2. Datsenko, N.V. Improving the efficiency of forming competencies in the field of information technology using an adaptive automated training system / N.V. Datsenko, S.A. Gorbatenko, V.V. Gorbatenko // Problems of teaching mathematics, physics, chemistry and computer science in universities and secondary schools (PPMFHI-VII): Proceedings of the VII regional scientific and methodological conference, Voronezh, April 24, 2021. Voronezh: Voronezh State University of Engineering Technologies, 2021;74-76.
3. Verbin, S.V. Science of decision-making / S.V. Verbin. St. Petersburg, Piter, 2012. 241 p.
4. Fuzzy Sets in Control Models and Artificial Intelligence / Ed. by D.A. Pospelov. M.: Nauka. Chief Ed. of Phys.-Math. Literature, 1986. 312 p.
5. Fuzzy Sets and Possibility Theory: Trans. from English / Ed. by R.R. Yager. M.: Radio and Communications, 1986. 408 p.
6. Datsenko, N.V. Adaptive Automated System as a Means of Differentiating Training in the Training of Specialists in the Field of Information Technology / N.V. Datsenko, S.A. Gorbatenko, V.V. Gorbatenko. Modeling, Optimization and Information Technology. 2019; 7(2 (25)):382-390. – DOI 10.26102/23106018/2019.25.2.007.
7. Gerchikova, I.N. The process of making and implementing management decisions. Management in Russia and Abroad. 2003;12: 39-42.
8. Datsenko, N.V. Algorithmic procedure for inaccurate inference for student certification in an adaptive automated system for teaching IT disciplines// Proceedings of the LXI reporting scientific conference of teachers and researchers of VSUET for 2022: In 3 parts, Voronezh, February 08-09, 2023. Voronezh: Voronezh State University of Engineering Technologies, 2023; 34-36.
9. Sutyagina N.I. Dynamic programming method for making microeconomic decisions // Bulletin of NGIEI. 2014. No. 11 (42). URL: https://cyberleninka.ru/article/n/metod-dinamicheskogo-programmirovaniya-priprinyatii-mikroekonomicheskogo-resheniya (date of access: 14.05.2025). Kulik S.D. Decision-making theory (elements of the theory of testing probable hypotheses): textbook. M .: MEPhI, 2007. 152 p.
10. Datsenko, N. V. Development of information support for an automated teaching system for the discipline "Computer Science" Public safety, legality and law and order in the III millennium. 2018; 4-2:28-31.
11. Chaadaev, K.V. Solving dynamic programming problems using network methods// Computer Science, Computer Engineering and Management. - 2022. No. 1. URL: https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=http://nauteh-journal.ru/files/700b5a07a4e3-49ad-8db8-398b163221b8&ved=2ahUKEwjP(дата обращения: 14.05.2025)
12. Borisov A.N. Processing of fuzzy information in decision-making systems / A.N. Borisov, A.V. Alekseev, G.V. Merkuryeva, et al. M.: Radio and Communications, 1989. 304 p.
13. Venttsel E.S. Operations Research: Tasks, Principles, Methodology. M.: Higher School, 2001.-208 p.
14. Datsenko, N.V. Application of Inaccurate Inference for Certification of Students in an Adaptive Automated System for Teaching IT Disciplines / N.V. Datsenko, S.A. Gorbatenko, V.V. Gorbatenko, G.V. Gorbatenko. Education and Law. 2022; 9: 255-260.
15. Datsenko, N.V. Formalization of criteria for assessing the academic performance of students in a distance learning format / N.V. Datsenko, S.A. Gorbatenko, V.V. Gorbatenko // Modeling of energy-information processes, Voronezh: Voronezh State University of Engineering Technologies, 2023; 271-277.
16. Borisov, A.N. Processing fuzzy information in decision-making systems / A.N. Borisov, A.V. Alekseev, G.V. Merkuryeva et al. Moscow: Radio and Communications, 1989. 304 p.
17. Orlovsky S.A. Problems of decision-making with fuzzy initial information. Moscow: Science, 1981:208.
18. Datsenko, N.V. Using an automated system to improve the quality of training specialists in the humanities / N.V. Datsenko // Information exchange processes in the activities of law enforcement agencies: current state and prospects for improvement. Collection of scientific articles. Orel, 2015: 46-48.
19. Matytsina, I.A. Methodology for selecting an employee based on fuzzy logic / I.A. Matytsina, L.A. Korobova. Bulletin of Tula State University. Technical sciences. 2023;11: 451-456. -EDN RHPFWY.
20. Porotikova, E.S. Personnel suitability for performing tasks in design organizations / E.S. Porotikova, L.A. Korobova, I.A. Matytsina // Automation and modeling in design and management: collection of scientific articles of the All-Russian conference, Bryansk, May 22, 2023. Kursk: ZAO "Universitetskaya kniga", 2023. Pp. 108-114. EDN LYKNZV.
21. Korobova, L.A. Analysis of the quality of fine milk filters / L.A. Korobova, I.A. Matytsina, E.S. Pracheva.Modeling of energy-information processes, Voronezh: Voronezh State University of Engineering Technologies, 2023; 349-354. EDN NEYRTF.
Review
For citations:
Datsenko N.V., Korobova L.A., Matytsina I.A. Application of algorithmic procedures of imprecise inference based on fuzzy knowledge to the solution of applied problems. Herald of Dagestan State Technical University. Technical Sciences. 2025;52(4):73-82. (In Russ.) https://doi.org/10.21822/2073-6185-2025-52-4-73-82
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