Submit your research to the International Journal "Notes on Intuitionistic Fuzzy Sets". Contact us at

Call for Papers for the 27th International Conference on Intuitionistic Fuzzy Sets is now open!
Conference: 5–6 July 2024, Burgas, Bulgaria • EXTENDED DEADLINE for submissions: 15 APRIL 2024.

Issue:Designing and developing Intuitionistic Fuzzy Logic Toolbox in MATLAB: Membership and non-membership functions gallery

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Jump to navigation Jump to search
Title of paper: Designing and developing Intuitionistic Fuzzy Logic Toolbox in MATLAB: Membership and non-membership functions gallery
Kaviranjanii G.
B. S. in Data Science and Applications, Indian Institute of Technology Madras, Chennai, Tamilnadu, India
Parvathi Rangasamy
Associate Professor and Head, Department of Mathematics, Vellalar College for Women (Autonomous), Erode - 638 107, Tamil Nadu, India
Presented at: Proceedings of the 27th International Conference on Intuitionistic Fuzzy Sets, 5–6 July 2024, Burgas, Bulgaria
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 30 (2024), Number 2, pages 142-155
Download:  PDF (193  Kb, Info)
Abstract: The authors have designed and developed algorithms for pattern recognition and clustering techniques using intuitionistic fuzzy (IF) sets, IF operators, IF logic (IFL) – shortest path in networks using IF graphs and IF hypergraphs – video processing using temporal IF sets, RGB image representation through IF index matrices, and molecular structure representation through IF directed hypergraphs. The three major steps involved in the above-said modeling processes via IFSs are (i) intuitionistic fuzzification, (ii) modification of membership and non- membership values (using IF logic/operators/rules/relations) and (iii) intuitionistic defuzzification. While developing these algorithms, parameter tuning was one of the major limitations, and hence specific values were assigned to complete the running process. To overcome this, it is necessary to introduce a toolbox in MATLAB so that the users can select the appropriate tools and parameterize them. Hence, in the long process of contributing a full-pledged intuitionistic fuzzy logic toolbox, namely IFL Toolbox in MATLAB, the membership and non-membership functions gallery has been developed initially, as one of the modules which is the foundation for any IFL control system. This module contains functions, codes, examples and figures/graphs, which will be available on the MATLAB creation page. The proposed module is compared with the existing fuzzy logic toolbox in MATLAB and verified.
Keywords: Membership and non-membership gallery, IFL Toolbox
AMS Classification: 03E72.
  1. Angelova, N. (2021). IFSTOOL – Software for Intuitionistic Fuzzy Sets Necessity, Possibility and Circle Operators. In: Atanassov, K., et al. (Ed.) Uncertainty and Imprecision in Decision Making and Decision Support: New Challenges, Solutions and Perspectives. IWIFSGN 2018. Advances in Intelligent Systems and Computing, vol 1081, 76–81. Springer, Cham.
  2. Atanassov, K. T. (1999). Intuitionistic Fuzzy Sets: Theory and Applications. Physica-Verlag, Heidelberg, New York.
  3. Atanassov, K. T. (2012). On Intuitionistic Fuzzy Sets Theory. Springer-Verlag, Berlin, Heidelberg, New York.
  4. Blishun, A. (1987). Fuzzy learning models in expert systems. Fuzzy Sets and Systems, 22(1), 57–70.
  5. Dubois, D., Lang, J., & Prade, H. (1991). Fuzzy sets in approximate reasoning, Part 2: Logical approaches. Fuzzy Sets and Systems, 40(1), 203–244.
  6. Dubois, D., & Prade, H. (1995). Comparison of two fuzzy set-based logics: similarity logic and possibility logic. Proceedings of the International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and the Second International Fuzzy Engineering Symposium, 20–24 March 1995, Yokohama, Japan, 4, 1219–1226.
  7. Kandel, A., & Byatt, W. (1978). Fuzzy sets, fuzzy algebra and fuzzy statistics. Proceedings of the IEEE, 66(12), 1619–1639.
  8. MathWorks.
  9. Parvathi, R. (2018). Designing and developing image editing tools in MATLAB using intuitionistic fuzzy sets. Major research project.
  10. Parvathi, R., Yuvapriya, C., & Maragatham, N. (2017). An application of IF directed hypergraph in molecular structure representation. Notes on Intuitionistics Fuzzy Sets, 23(2), 69–78.
  11. Radhika, C., & Parvathi, R. (2016). Defuzzification of intuitionistic fuzzy sets. Notes on Intuitionistic Fuzzy Sets, 22(5), 19–26.
  12. Radhika, C., & Parvathi, R. (2016). Intuitionistic fuzzification functions. Journal of Pure and Applied Mathematics, 12(2), 1211–1227.
  13. Witold, P. (1984). Identification in fuzzy systems. IEEE Transactions on Systems and Cybernetics, 14(2), 361–366.
  14. Yager, R. R., & Filev, D. P. (1993). SLIDE: A simple adaptive defuzzification method. IEEE Transactions on Fuzzy Systems, 1(1), 69–78.

The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.