Applications of Fuzzy Logic in Modern Technology

Authors

https://doi.org/10.48314/jidcm.v1i1.59

Abstract

Fuzzy logic is a form of many-valued logic that deals with reasoning that is approximate rather than fixed and exact. This article explores the various applications of fuzzy logic across different fields, including control systems, decision-making, artificial intelligence, and more. By examining these applications, we can appreciate the versatility and effectiveness of fuzzy logic in solving complex problems.

Keywords:

Fuzzy logic, Artificial intelligence, Decision-making, Control systems, Intelligent technologies, Modern applications

References

  1. [1] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338--353. https://doi.org/10.1016/S0019-9958(65)90241-X
  2. [2] Chang, P. T., & Lee, E. S. (1999). Fuzzy decision networks and deconvolution. Computers & mathematics with applications, 37(11), 53–63. https://doi.org/10.1016/S0898-1221(99)00143-1
  3. [3] Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods. In Fuzzy multiple attribute decision making: Methods and applications (pp. 289-486). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-46768-4_5
  4. [4] Dubois, D., & & Prade, H. (1984). Fuzzy logics and the generalized modus ponens revisited. Cybernetics and systems, 15(3–4), 293–331. https://doi.org/10.1080/01969728408927749
  5. [5] Didier Dubois. (1988). Possibility theory: An approach to computerized processing of uncertainty. New York: Plenum Press. https://archive.org/details/possibilitytheor0000dubo
  6. [6] Ekong, V. E., Inyang, U. G., & Onibere, E. A. (2012). Intelligent decision support system for depression diagnosis based on neuro-fuzzy-CBR hybrid. Modern applied science, 6(7), 79–88. https://www.academia.edu/download/87693574/12094.pdf
  7. [7] Fu, X., Zeng, X. J., Luo, X., Wang, D., Xu, D., & Fan, Q. L. (2017). Designing an intelligent decision support system for effective negotiation pricing: A systematic and learning approach. Decision support systems, 96, 49–66. https://doi.org/10.1016/j.dss.2017.02.003
  8. [8] Gentili, P. L. (2017). A strategy to face complexity: The development of chemical artificial intelligence. Communications in computer and information science (pp. 151–160). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-57711-1_13
  9. [9] Gentili, P. L. (2008). Boolean and fuzzy logic gates based on the interaction of flindersine with bovine serum albumin and tryptophan. The journal of physical chemistry a, 112(47), 11992–11997. https://doi.org/10.1021/jp806772m
  10. [10] Molin, M. D., & Masella, C. (2016). From fragmentation to comprehensiveness in network governance. Public organization review, 16(4), 493–508. https://doi.org/10.1007/s11115-015-0320-4
  11. [11] Ožbot, M., Lughofer, E., & Škrjanc, I. (2023). Evolving neuro-fuzzy systems-based design of experiments in process identification. IEEE transactions on fuzzy systems, 31(6), 1995–2005. https://doi.org/10.1109/TFUZZ.2022.3216992

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Published

2025-03-08

Issue

Vol. 1 No. 1 (2025): Journal of Intelligent Decision and Computational Modelling

Section

Articles

How to Cite

Shahamat, M. . (2025). Applications of Fuzzy Logic in Modern Technology. Journal of Intelligent Decision and Computational Modelling, 1(1), 27-34. https://doi.org/10.48314/jidcm.v1i1.59