HE METHOD OF FUZZY SET-ANALYSIS OF ECONOMIC REGIONS

Authors

  • Simon Kuznets Kharkiv National University of Economics

Abstract

The methodology of fuzzy set-state analysis of economic regions is offered in this paper. It is designed in order to formalize the approach to solving complex multi task planning their further development. The methodology to determine probabilistic fuzzy performance characteristics of different nature, it allows to avoid limitations and ambiguity that arises in rolls partial criteria in a global quality criterion. Some benchmarks can be described not as a quantitative form, but as a guest expert expressed only verbally. The methodology allows to limit the use of peer reviews, which are used in determining the parameters that can not be obtained with the required reliability. This may prevent repeated use of experts at various levels of complex hierarchical system of evaluating economic status of regions. Otherwise it can lead to a significant reduction of global uncertainty prevailing criteria. Application the methodology avoids eliminate the need for determining the significance of each of the economic performance of regions. The use of methodology is possible for comparing the economic status of the region, and for evaluating the relative growth economic situation the same region at different times.

Keywords: economic condition, fuzzy set, multi criteria evaluation, membership function, linguistic variable.

Author Biography

, Simon Kuznets Kharkiv National University of Economics

кандидат технічних наук, доцент

References

Література

Недосекин А.О. Нечетко-множественный анализ рисков фондовых инвестиций. – СПб.: «Сезам», 2002.– 181 с.

Дилигенский Н.В., Дымова Л.Г., Севастьянов П.В. Нечеткое моделирование и много-критериальная оптимизация производственных систем в условиях неопределенности: технология, экономика, экология М.: «Издательство Машиностроение − 1», 2004. – 397 с.

Севастьянов П.В., Туманов Н.В. Многокритериальная идентификация и оптимизация технологических процессов. Минск: Наука и техника, 1999. 224 с.

Севастьянов П., Севастьянов Д. Методическое и программное обеспечение финансово-экономического анализа в условиях неопределенности исходных данных /Информационно-аналитические системы в финансовой деятельности //Тезисы докладов Первого Белорусского Форума. Минск, 1997. С. 50 - 55.

Dubois D., Koenig J.L. Social choice axioms for fuzzy set aggregation // Fuzzy Sets and Systems. 2001. V. 43. P. 257 – 274.

Лосєв М.Ю. Нечітко-множинна оцінка стану параметрів техніко-економічних систем / М.Ю. Лосєв, Ю.М. Малишко // Системи обробки інформації: збірник наукових праць. – Х.: Харківський університет Повітряних сил імені Івана Кожедуба, 2015. – Вип.4 (129). – С.33-38

Лосев М. Ю. Методика многокритериальной оценки состояния технико-экономических систем на основе нечетко-множественного анализа показателей // Лосев М.Ю., Кононов Ю.Н., Лосева Ю.М. — Х.: ХУПС, 2012. — 232 с.(С.24-29).

Ковалев В. В. Финансовый анализ: управление капиталом, выбор инвестиций, анализ отчётности. - М.: Финансы и статистика, 2000. –512с.

Калмыков С.А., Шокин Ю.И., Юлдашев З.Х. Методы интервального анализа.– Новосибирск: «Наука», 2006. – 223 с.

Heilpern S. Representation and application of fuzzy numbers // Fuzzy sets and Systems. 2007. № 91. P. 259-268.

Ishihashi H., Tanaka M. Multiobjective programming in optimization of the Interval Objective Function // European Journal of Operational Research. 2000. № 48. P. 219-225.

Walster G.W., Bierman M.S. Interval Arithmetic in Forte Developer Fortran // Technical Report. Sun Microsystems. March 2010. P. 35-43.

Moore R.E. Interval analysis. Englewood Cliffs. N.J.: Prentice-Hall, 1966. 250 p.

Sevastjanov Pavel V., Rog Pawel, Venberg Andrej V. A Constructive Numerical Method for the Comparison of Intervals // Parallel Processing and Applied Mathematics. 4th International Conference. PPAM 2001. Naleczow, Poland, September 2001, Revised Papers. 2001. P. 756-761.

References

Nedosekyn, А.О. (2002). Fuzzy-multiple analysis of stock investment risks. Sesame, 181.

Diligensky, N.V., Dymova, L.G., Sevastyanov P.V. (2004). Fuzzy modeling and multi-criteria optimization of production systems in conditions of uncertainty: technology, economics, ecology. Izdanie Mashinostroenie-1, 397.

Sevastyanov, P.V., Tumanov, N.V. (1999). Multi-criteria identification and optimization of technological processes. Science and Technology, 224.

Sevastyanov, P.V., Sevastyanov, D. P. (2001). Methodical and software support of financial and economic analysis in conditions of uncertainty of initial data. Informational and analytical systems in financial activity, 50-55.

Dubois, D., Koenig, J.L. (2001). Social choice axioms for fuzzy set aggregation. Fuzzy Sets and Systems, 43, 257–274.

Losev, M.Y., Malyshko, Y.M. (2015). Fuzzy-evaluation of multiple parameters of feasibility. Information processing systems: technologies, 4 (129), 33-38

Losev, M.Y., Kononov, Y.N., Losevа, Y.M. (2012). The method of multicriteria estimation of the state of technical and economic systems based on fuzzy-multiple analysis of indicators. HUPS, 24-29.

Kovalev, V. V. (2000). Financial analysis: management of capital, the choice of investment, analysis of reporting. Finance and Statistics, 512.

Kalmykov, S.A, Shokin, Y.I., Yuldashev, Z.K. (2006). Methods of interval analysis. Science, 223 p.

Heilpern, S. (2007). Representation and application of fuzzy numbers. Fuzzy sets and Systems, 91, 259-268.

Ishihashi, H., Tanaka, M. (2000). Multiobjective programming in optimization of the Interval Objective Function. European Journal of Operational Research, 48, 219-225.

Walster, G.W., Bierman, M.S. (2010). Interval Arithmetic in Forte Developer Fortran. Technical Report. Sun Microsystems, 35-43.

Moore, R.E. (1966). Interval analysis. Englewood Cliffs. Prentice-Hall, 250.

Sevastjanov, P.V., Rog, P.V., Venberg, A.V. (2001). Constructive Numerical Method for the Comparison of Intervals. Parallel Processing and Applied Mathematics, 756-761.

Published

2017-05-24

How to Cite

(2017). HE METHOD OF FUZZY SET-ANALYSIS OF ECONOMIC REGIONS. Municipal Economy of Cities, (133), 19–26. Retrieved from https://khg.kname.edu.ua/index.php/khg/article/view/4983