Comparison of fuzzy logic systems based on triangular and trapezoidal membership functions — Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki

1. Tarasyan V.S. Paket Fuzzy Logic Toolbox for MatLab. Uchebnoe posobie [Fuzzy Logic Toolbox for Matlab package: textbook for the courses]. Ekaterinburg, UrGUPS Publ., 2013. 112 p. (in Russ).
2. Caymaz T., Çalişkan S., Botsali A.R. Evaluation of ergonomic conditions using fuzzy logic in a metal processing plant. International Journal of Computational and Experimental Science and Engineering, 2022, vol. 8, no. 1, pp. 19–24.
3. Zhang T., Liu Y., Rao X., Li X. Optimal design of building environment with hybrid genetic algorithm, artificial neural network, multivariate regression analysis and fuzzy logic controller. Building and Environment, 2020, vol. 175, pp. 106810.
4. Ali O.A.M., Ali A.Y., Sumait B.S. Comparison between the effects of different types of membership functions on fuzzy logic controller performance. International Journal, 2015, vol. 76, pp. 76–83.
5. Prajapati S., Fernandez E. Performance evaluation of membership function on fuzzy logic model for solar PV array. 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON). IEEE, 2020, pp. 609–613.
6. Khairuddin S.H., Hasan M.H., Hashmani M.A., Azam M.H. Generating clustering-based interval fuzzy type-2 triangular and trapezoidal membership functions: A structured literature review. Symmetry, 2021, vol. 13, no. 2, pp. 239. DOI: 10.3390/sym13020239.
7. Talpur N., Salleh M.N., Hussain K. An investigation of membership functions on performance of ANFIS for solving classification problems. IOP Conference Series: Materials Science and Engineering, 2017, vol. 226, pp. 012103. Available at: https://www.researchgate.net/publi-cation/319107925, free (Accessed: December 20, 2023).
8. Sadollah A. Introductory chapter: which membership function is appropriate in fuzzy system? Fuzzy logic based in optimization methods and control systems and its applications, 2018. Available at: https://www.researchgate.net/publication/328643706, free (Accessed: December 20, 2023).
9. Avdeeva V.I., Knyazeva I.V. Ocenka effectivnosti sistmy sohranenya zdorovya v regione na osnove ispolzovaniya metodov nechetkoy logiki [Assessing the efficiency of the health preservation system in zones based on the use of fuzzy logic methods]. Economics: Yesterday, Today, Tomorrow, 2019, vol. 9, no. 5-1, pp. 395–404 (in Russ)
10. Kolodnikov M.I., Efermov A.A. Postroenie polzovatelskih funktsiy prinadlezhnosty v pakete MatLab [Construction of custom membership functions in the MatLab package]. Youth and Modern Information Technologies: Collection of Proceedings of the XIV International Scientific and Practical Conference of Students, Postgraduate Students and Young Scientists, 2016, vol. 1, pp. 352–353 (in Russ).
11. Meshkovsky E.O., Kurmashev A.D., Frolov V.Y. Nechetkoe soglasovannoe upravlenie sistemoy electroprivodov chetirehkolesnogo mobilnogo robota [Fuzzy coordinated control of the electric drive system of a four-wheeled mobile robot]. Proceedings of TUSUR University, 2020, vol. 23, no. 3, pp. 61–69 (in Russ.).
12. Krishnan R.S., Sekar R. Fuzzy logic based smart irrigation system using internet of things. Journal of Cleaner Production, 2020, vol. 252, pp. 119902
13. Gallab M., Bouloiz H., L. Alaoui Y., Tkiouat M. Risk assessment of maintenance activities using fuzzy logic. Procedia Computer Science, 2019, vol. 148, pp. 226–235.
14. de Miranda R.V., Inácio P.R.M., Magoni D., Freire M.M. Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms. Computer Networks, 2021, vol. 186, pp. 107792.
15. Lau H.C.W., Cheng E.N.M., Lee C., Ho G.T.s. A fuzzy logic approach to forecast energy consumption change in a manufacturing system. Expert Systems with Applications, 2008, vol. 34, no. 3, pp. 1813–1824.
16. Zubkova Tatiana M.T., Ishakova E.N. Avtomatizaciya upravleniya riskami progrmmnikh proektov na osnove nechetkogo logicheskogo vivoda [Risk management automation of software projects based on fuzzy inference]. Journal Scientific and Technical of Information Technologies, Mechanics and Optics, 2015, vol. 99, no. 5, pp. 877–885 (in Russ.).
17. Yazdi K., Hanne A., Wang T., Hui W. A credit rating model in a fuzzy inference system environment. Algorithms, 2019, vol. 12, no. 7, pp. 139.
18. Ergüzel O.Ş., Tunahan H., Sinan E. A Different Approach to Global Supplier Risk: A Finance Based Model. Alphanumeric Journal, 2019, vol. 7, pp. 11–24.
19. Pamucar D., Ćirović G. Vehicle route selection with an adaptive neuro fuzzy inference system in uncertainty conditions. Decision Making: Applications in Management and Engineering, 2018, vol. 1, no. 1, pp. 13–37.
20. Sremac S., Tanackov I., Kopić M., Radović D. ANFIS model for determining the economic order quantity. Decision Making: Applications in Management and Engineering, 2018, vol. 1, no. 2, pp. 81–92.
21. Sharma S., Obaid A.J. Mathematical modelling, analysis and design of fuzzy logic controller for the control of ventilation systems using MATLAB fuzzy logic toolbox. Journal of Interdisciplinary Mathematics, 2020, vol. 23, no. 4, pp. 843–849.
22. Janarthanan R., Balamurali R., Annapoorani A., Vimala V. Prediction of rainfall using fuzzy logic, Materials Today: Proceedings, 2021, vol. 37, pp. 959–963
23. Ahaev A.V. Algoritm ocenivaniya funkcionalnogo napolneniya programmnikh produktov na osnove nechetkogo logich-eskogo vivoda [Algorithm for evaluating the functional content of software products based on fuzzy logical inference]. Proceedings of TUSUR University, 2013, no. 2 (28), pp. 169–174 (in Russ.).
24. Pamučar D., Ljubojević S., Kostadinović D., Đorović B. Cost and risk aggregation in multi-objective route planning for hazardous materials transportation–A neuro-fuzzy and artificial bee colony approach. Expert Systems with Applications, 2016, vol. 65, pp. 1–15
25. Bylykbashi K., Qafzezi E., Ampririt P., Ikeda M., Matsuo K., Barolli L. Performance evaluation of an integrated fuzzy-based driving support system for real-time risk management in VANETs. Sensors, 2020, vol. 20, no. 22, pp. 6537.
26. Wu B., Yip T.L., Yan X., Soares C.G. Fuzzy logic based approach for ship-bridge collision alert system. Ocean Engineering, 2019, vol. 187, pp. 106152.
27. Wu B., Cheng T., Yip T.L., Wang Y. Fuzzy logic based dynamic decision-making system for intelligent navigation strategy within inland traffic separation schemes. Ocean Engineering, 2020, vol. 197, pp. 106909.
28. Glushenko S.A. Primenenie mekhanizma nechetkoy logiki dlya ocenki riska investicionno-stroitelnikh proektov [Application of the fuzzy logic mechanism for risk assessment of investment and construction projects]. Vestnik of Rostov State Economic University (RINH), 2014, vol. 3, pp. 58–68 (in Russ.).
29. Glushenko S.A., Dolzhenko A.I. Sistema nechetkogo modelirovaniya riskov investicionn-stroitelnikh proektov [System of the fuzzy logic for risk assessment of investment and construction projects]. Vestnik of Rostov State Economic University (RINH), 2015, vol. 2 (32), pp. 48–58 (in Russ.).
30. Castro J.L. Fuzzy logic controllers are universal approximators. IEEE Transactions on Systems, Man, and Cybernetics, 1995, vol. 25, vol. 4, pp. 629–635
31. Yeh C.T. Trapezoidal and triangular approximations preserving the expected interval. Fuzzy Sets and Systems, 2008, vol. 159, no. 11, pp. 1345–1353.
32. Yeh C.T. Existence of interval, triangular, and trapezoidal approximations of fuzzy numbers under a general condition. Fuzzy Sets and Systems, 2017, vol. 310, pp. 1–13.
33. Princy S., Dhenakaran S.S. Comparison of triangular and trapezoidal fuzzy membership function. Journal of Computer Science, 2016, vol. 2, no. 8, pp. 46–51.
34. Coroianu L. Trapezoidal approximations of fuzzy numbers using quadratic programs. Fuzzy Sets and Systems, 2021, vol. 417, pp. 71–92.
35. Estdale J., Georgiadou E. Applying the ISO/IEC 25010 quality models to software product. Systems, Software and Services Process Improvement: 25th European Conference, EuroSPI 2018, Proceedings 25. Springer International Publishing, 2018, pp. 492–503.
36. Mandal S., Maiti J. Risk analysis using FMEA: Fuzzy similarity value and possibility theory-based approach. Expert Systems with Applications, 2014, vol. 41, no. 7, pp. 3527–3537.
37. Çakır E., Ulukan Z. Digitalization on Aviation 4.0: Designing a Scikit-Fuzzy control system for in-flight catering customer satisfaction. Intelligent and Fuzzy Techniques in Aviation 4.0: Theory and Applications. Cham: Springer International Publishing, 2021, pp. 123–146.
38. Cingolani P., Alcala-Fdez J. JFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation. Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on Fuzzy Systems, 2012, pp. 1–8. DOI: 10.1109/FUZZ-IEEE.2012.6251215.
39. Mamdani E.H. Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man-Machine Studies, 1976, vol. 8, no. 6, pp. 669–678.