Published December 29, 2020 | Version v1
Journal article Open

Relevance of the application of the theory of fuzzy sets in the calculation of the strategic security of a complex technical system

Creators

  • 1. Admiral Makarov State University of Maritime and Inland Shipping (St. Petersburg)

Description

The relevance of the topic is related to the problem of protection and security of technical systems in the era of globalization and information warfare, as well as determining the strategic reserve of stability for production cycles. The study object was mathematical methods of modelling production processes and determining the point of the production system stability. The study purpose was to use the fuzzy set device to determine the point of stability of the production system. To implement this study, methods of statistical analysis, data grouping, sample ranking, and methods for studying time series components were used. During the study, the scientific materials of leading researchers in the field of fuzzy sets and mathematical methods to calculate various components were used. The study results are intended for specialists and researchers in development and application of mathematical methods in the modelling of economic indicators of technological processes at enterprises.

Актуальность темы связана с проблемой защиты и безопасности технических систем в эпоху глобализации и информационной войны, а также определения стратегического запаса стабильности для производственных циклов. Объектом исследования стали математические методы моделирования производственных процессов и определение точки стабильности производственной системы. Целью исследования являлось применение аппарата нечётких множеств для определения точки стабильности производственной системы. Для реализации данного исследования были применены методы статистического анализа, группировка данных, ранжирование выборки, методы исследования компонент временного ряда. В ходе исследования были использованы научные материалы ведущих исследователей в области нечётких множеств и математических методов расчётах различных компонентов. Результаты исследования предназначаются для специалистов и исследователей в области разработки и применения математических методов в моделировании экономических показателей технологических процессов на предприятиях.

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References

  • Aleskovsky, V. V., & Yantsen A. V. (2011). Russian experience in developing a program-but-target approach. Almanac of Modern Science and Education, 4(47), 156-158. (in Russian)
  • Bellman, R. E., & Zadeh, L. A. (1970). Decision making in a fuzzy environment. Management Science, 17(4), 141-164.
  • Bellman, R. E. & Zadeh, L. A. (1977). Local and fuzzy logics. Modern Uses of Multiple-Valued Logic, 103-165. The Netherlands: D. Reidel Publishing Co.
  • Chernov, V. P. (2002). Mathematics for senior managers. St Petersburg: Nauka. (in Russian)
  • Golik, E. S. (Ed.) (2005). Mathematical methods of system analysis and decision-making theory. Part II: Textbook. St. Petersburg: SZTU. (in Russian)
  • Guts, A. K. (2016). Mathematical logic and the theory of algorithms. Textbook. Ed. 3rd, ISPR. Moscow: LENAND, 2016. (in Russian)
  • International collection of Newspapers of the 19th and 20th century. (n.d.). (in Russian). https://freep.newspapers.com/
  • Kim, E. S. (2016). Analysis of existing software to automate the operation of the enterprise. Technology. Technologies. Engineering, 1, 11-14.
  • Kolesnichenko, S. V. (2017). Information and statistical analysis of the processes of functioning of complex technical and socio-economic systems. Proceedings of the 16th MNPK Analysis and Forecasting of Control Systems in Industry and Transport, 336-348. St. Petersburg.: Fedorov Publishing House. (in Russian)
  • Kolmogorov, A. N., & Dragalin, A. G. (2017). Mathematical logic: Introduction to mathematical logic. Textbook. Moscow: LENAND. (in Russian)
  • Komissarov, P. V. (2018). Actual issues of modelling production processes of complex technical and socio-economic systems. Collection of Works of the GUMRF. St. Petersburg: GUMRF, 67-71. (in Russian)
  • Komissarov, P. V. (2018). Method of complex assessment of economic indicators of the enterprise. Collection: Analysis and Forecasting of Management Systems in Industry and Transport, 57-58. St. Petersburg: Astra. (in Russian)
  • Krass, M. S., & Chuprykov, B. P. (2019). Mathematics in Economics: mathematical methods and models. Moscow: Yurayt Publishing House.
  • Leonenkov, A. V. (2003). Fuzzy modeling in MATLAB and fuzzy TECH. St Petersburg: BHV-Petersburg. (in Russian)
  • Lisitsina, L. S. (2020). Fundamentals of fuzzy set theory. St Petersburg: University ITMO. (in Russian)
  • Pyanykh, S. M. (1988). Economic and mathematical methods of optimal planning of river transport. Moscow: Transport. (in Russian)
  • Siegfried, G. (2010). An early approach toward graded identity and graded membership in set theory. Fuzzy Sets and Systems, 161(18), 2369-2379.
  • Volokhin, D. V. (2020). The relevance of the representation of fuzzy knowledge using fuzzy sets. Scientific Aspect, 2(16), 2026-2029. Samara: Aspect. (in Russian)
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
  • Zadeh, L. A., Fu, K., Tanaka, K., & Shimura M. (Eds.). (1975). Fuzzy sets and their applications to cognitive and decision processes. New York: Academic Press.
  • Zadeh, L. A., & Polak, E. (Eds.). (1969). System Theory. Inter-University Electronics Series, 8. New York: McGraw-Hill.
  • Zemskov, A. V., & Shilkina I. D. (2018). Economic and mathematical models. Educational and methodological guide for laboratory work. St. Petersburg: Publishing house of S. O. Makarov GUMRF. (in Russian)