Статья: ПРИМЕНЕНИЕ МЕТОДОВ НЕЧЕТКОЙ ЛОГИКИ И МАШИННОГО ОБУЧЕНИЯ ДЛЯ АНАЛИЗА ПРОМЫШЛЕННОГО ЭЛЕКТРОПОТРЕБЛЕНИЯ В УСЛОВИЯХ НЕОПРЕДЕЛЕННОСТИ (2024)

1. Zhaoying O. Construction and Application of Economic Management Fuzzy Decision Model Based on Fuzzy Relevance Method // Journal of Mathematics. 2022. Vol. 2022. Article ID 9878815. 11 p. DOI: 10.1155/2022/9878815
2. Jang J.-S. R. ANFIS: adaptive-network-based fuzzy inference system // IEEE Transactions on Systems, Man and Cybernetics. 1993. Vol. 23, iss. 3. P. 665-685. DOI: 10.1109/21.256541
3. Abraham A. Adaptation of Fuzzy Inference System Using Neural Learning // Nedjah N., Macedo Mourelle L. (eds) Fuzzy Systems Engineering. Studies in Fuzziness and Soft Computing. Vol. 181. Berlin: Springer, 2005. P. 53-83. DOI: 10.1007/11339366_3
4. Giles D. E. A. Output Convergence and International Trade: Time-Series and Fuzzy Clustering Evidence for New Zealand and her Trading Partners, 1950 - 1992 // Journal of International Trade and Economic Development. 2005. Vol. 14, iss. 1. P. 93-114. DOI: 10.1080/0963819042000333261
5. Лебедева М. Нечеткая логика в экономике - формирование нового направления // Идеи и идеалы. 2019. Т. 11, № 1-1. С. 197-212. DOI: 10.17212/2075-0862-2019-11.1.1-197-212 EDN: ZCWNFJ
6. Giles E. A., Draeseke R. Econometric Modeling Based on Pattern Recognition via the Fuzzy C-Means Clustering Algorithm. Working Paper EWP0101 // Department of Economics, University of Victoria. 2001. 50 p. URL: https://www.uvic.ca/socialsciences/economics/_assets/docs/econometrics/ewp0101.pdf (дата обращения: 20.01.2023).
7. Takagi T., Sugeno M. Fuzzy identification of systems and its applications to modeling and control // IEEE Transactions on Systems, Man, and Cybernetics. 1985. Vol. SMC-15, no. 1. P. 116-132. DOI: 10.1109/TSMC.1985.6313399
8. Xiao Y., Liu J. J., Hu Y., Wang Y., Lai K. K., Wang S. A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting // Journal of Air Transport Management. 2014. Vol. 39. P. 1-11. DOI: 10.1016/j.jairtraman.2014.03.004
9. Kumar S., Singh R., Manish K., Ashish K. Fuzzy Logic based Model to Calculate the Economic Level of any Country // International Journal of Basic Sciences and Applied Computing (IJBSAC). Vol. 1, iss. 12. P. 6-12. URL: https://www.ijbsac.org/wp-content/uploads/papers/v1i12/L00570311216.pdf (дата обращения: 20.01.2023).
10. Петров М. Б., Серков Л. А., Кожов К. Б. Оценка способов энергообеспечения полуострова Ямал на основе нечеткого многокритериального анализа // Экономика региона. 2022. Т. 18, № 4. С. 1209-1222. DOI: 10.17059/ekon.reg.2022-4-17 EDN: BAVQIV
11. Shipley M., Johnson M., Pointer L., Yankov N. A fuzzy attractiveness of market entry (FAME) model for market selection decisions // Journal of the Operational Research Society. 2013. Vol. 64, iss. 4. P. 597-610. DOI: 10.1057/jors.2012.59
12. Neocleous C. K., Schizas C. N., Papaioannou M. C. Fuzzy cognitive maps in estimating the repercussions of oil/gas exploration on politico-economic issues in Cyprus // 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011). Taipei, Taiwan, 2011. P. 1119-1126. DOI: 10.1109/FUZZY.2011.6007655
13. Cummins J. D., Derring R. A. Fuzzy trends in property-liability insurance claim costs // The Journal of Risk and Insurance. 1993. Vol. 60, no. 3. P. 429-465. DOI: 10.2307/253037 EDN: EPXLUT
14. Целых А. Н., Целых Л. А., Причина О. С. Методы нечеткой логики в управлении производственными процессами // Известия ЮФУ. Технические науки. 2014. № 1 (150). С. 111-119. EDN: RTZMYH
15. Alaqeel T., Suryanarayanan S. A fuzzy Analytic Hierarchy Process algorithm to prioritize Smart Grid technologies for the Saudi electricity infrastructure // Sustainable Energy, Grids and Networks. 2018. Vol. 13. P. 122-133. DOI: 10.1016/j.segan.2017.12.010
16. Mahjouri M., Ishak M., Torabian A., Manaf L., Halimoon N., Ghoddusi J. Optimal selection of Iron and Steel wastewater treatment technology using integrated multi-criteria decision-making techniques and fuzzy logic // Process Safety and Environmental Protection. 2017. Vol. 107. P. 54-68. DOI: 10.1016/j.psep.2017.01.016
17. Zimmer K., Fröhling M., Breun P., Schultmann F. Assessing social risks of global supply chains: A quantitative analytical approach and its application to supplier selection in the German automotive industry // Journal of Cleaner Production. 2017. Vol. 149. P. 96-109. DOI: 10.1016/j.jclepro.2017.02.041
18. Беллман Р., Заде Л. Принятие решений в расплывчатых условиях // Вопросы анализа и процедуры принятия решений. М.: Мир, 1976. С. 172-215.
19. Леоненков А. Нечеткое моделирование в среде Matlab и fuzzyTECH. СПб.: БХВ-Петербург, 2005. 719 с.
20. Breiman L. Random forest // Machine Learning. 2001. Vol. 45. P. 5-32. DOI 10.1023/A:1010933404324. EDN: ARROTH
21. Svetnik V., Liaw A., Tong C., Culberson J. C., Sheridan R. P., Feuston B. P. Random forest: A classification and regression tool for compound classification and QSAR modeling // Journal of Chemical Information and Computer Sciences. 2003. Vol. 43, iss. 6. P. 1947-1958. DOI: 10.1021/ci034160g
22. Петров М. Б., Серков Л. А., Кожов К. Б. Анализ пространственных особенностей регионального электропотребления в РФ // Прикладная эконометрика. 2021. № 1 (61). С. 5-27. DOI: 10.22394/1993-7601-2021-61-5-27 EDN: PGLLIM
23. Chiu S. Fuzzy Model Identification Based on Cluster Estimation // Journal of Intelligent and Fuzzy Systems. 1994. Vol. 2, iss. 3. P. 267-278. DOI: 10.3233/IFS-1994-2306