Submit your research to the International Journal "Notes on Intuitionistic Fuzzy Sets". Contact us at nifs.journal@gmail.com

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:A method for graphical representation of membership functions for intuitionistic fuzzy inference systems

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Jump to navigation Jump to search
shortcut
http://ifigenia.org/wiki/issue:nifs/23/2/79-87
Title of paper: A method for graphical representation of membership functions for intuitionistic fuzzy inference systems
Author(s):
Oscar Castillo
Tijuana Institute of Technology,, Tijuana BC México
ocastillo@tectijuana.mx
Amaury Hernandez-Aguila
Tijuana Institute of Technology,, Tijuana BC México
Mario Garcia-Valdez
Tijuana Institute of Technology,, Tijuana BC México
Presented at: 21st International Conference on Intuitionistic Fuzzy Sets, 22–23 May 2017, Burgas, Bulgaria
Published in: "Notes on IFS", Volume 23, 2017, Number 2, pages 79—87
Download:  PDF (157 Kb  Kb, Info)
Abstract: This work proposes an approach for graphically representing intuitionistic fuzzy sets for their use in Mamdani fuzzy inference systems. The proposed approach is used, and plots for several membership and non-membership functions are presented, including: triangular, Gaussian, trapezoidal, generalized bell, sigmoidal, and left-right functions. Plots of some operators used in fuzzy logic are also presented, i.e., union, intersection, implication and alphacut operators. The proposed approach should produce plots that are clear to understand in the design of an intuitionistic fuzzy inference system, as the membership and non-membership functions are clearly separated and can be plotted in the same figure and still be recognized with ease.
Keywords: Fuzzy inference systems, Intuitionistic fuzzy logic, Membership function.
AMS Classification: 03E72
References:
  1. Akram, M., Habib, S., & Javed, I. (2014). Intuitionistic fuzzy logic control for washingmachines. Indian Journal of Science and Technology, 7(5), 654–661.
  2. Angelov, P. (1995). Crispification: defuzzification over intuitionistic fuzzy sets. Bulletinfor Studies and Exchanges on Fuzziness and its Applications (BUSEFAL), 64, 51–55.
  3. Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.
  4. Atanassov K. T. (2003). Intuitionistic fuzzy sets: past, present and future. In EUSFLATConf., 12–19.
  5. Atanassova, V. (2010). Representation of fuzzy and intuitionistic fuzzy data by Radarcharts. Notes on Intuitionistic Fuzzy Sets, 16(1), 21–26.
  6. Baccour, L., Kanoun, S., Maergner, V., & Alimi, A. M. (2008). An application of intuitionistic fuzzy information for handwritten Arabic word recognition. Notes on Intuitionistic Fuzzy Sets, 14(2), 67–72.
  7. Castillo, O., Alanis, A., Garcia, M., & Arias, H. (2007). An intuitionistic fuzzy system for time series analysis in plant monitoring and diagnosis. Applied Soft Computing, 7(4), 1227–1233.
  8. Chakarska, D. D., & Antonov, L. S. (1995). Application of intuitionistic fuzzy sets in plant tissue culture and in invitro selection. Notes on Intuitionistic Fuzzy Sets, 16(1),70–73.
  9. Cuvalcioglu G., Yilmaz S., & Bal, A. (2015). Some algebraic properties of the matrix representation of intuitionistic fuzzy modal operators. Notes on Intuitionistic Fuzzy Sets, 21(2), 6–18.
  10. Davarzani, H., & Khorheh, M. A. (2013). A novel application of intuitionistic fuzzy sets theory in medical science: Bacillus colonies recognition. Artificial Intelligence Research, 2(2), 1–17.
  11. Deschrijver, G., Cornelis, C., & Kerre, E. E. (2004). On the representation of intuitionistic fuzzy t-norms and t-conorms. IEEE Transactions on FuzzySystems, 12(1), 45–61.
  12. Despi, I., Opris, D., & Yalcin, E. (2013). Generalised Atanassov intuitionistic fuzzy sets. Proceeding of the Fifth International Conference on Information, Process, and Knowledge Management, France, 51–56.
  13. Hernandez-Aguila, A., & Garcia-Valdez, M. (2016). A proposal for an in tuitionisticfuzzy inference system. IEEE World Congress on Computational Intelligence. (To be published).
  14. Karnik, N. N., & Mendel, J. M. (2001). Centroid of a type-2 fuzzy set. Information Sciences, 132(1), 195–220.
  15. Own, C. M. (2009). Switching between type-2 fuzzy sets and intuitionistic fuzzy sets: an application in medical diagnosis. Applied Intelligence, 31, 283–291.
  16. Parvathi, R., Thilagavathi, S., Thamizhendhi, G., & Karunambigai, M. G. (2014). Index matrix representation of intuitionistic fuzzy graphs. Notes on Intuitionistic Fuzzy Sets, 20(2), 100–108.
  17. Sharma, P. K. (2011). Cut of intuitionistic fuzzy groups. International Mathematical Forum, 6(53), 2605–2614.
  18. Sharma, P. K. (2011). Cut of intuitionistic fuzzy modules. International Journal of Mathematical Sciences and Applications, 1(3), 1489–1492.
  19. Szmidt, E., & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems, 114(3), 505–518.
  20. Szmidt, E., & Kacprzyk, J. (2001). Intuitionistic fuzzy sets in some medical applications. In Computational Intelligence. Theory and Applications (148–151). Springer, Berlin, Heidelberg.
  21. Xu, Z. (2007). Intuitionistic preference relations and their application in group decision making. Information Sciences, 177(11), 2363–2379.
  22. Yilmaz, S., Citil, M., & Cuvalcioglu, G. (2015). Some properties of the matrix representation of the intuitionistic fuzzy modal operators. Notes on Intuitionistic Fuzzy Sets, 21(2), 19–24.
  23. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
  24. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199–249.
Citations:

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