Cubic Intuitionistic Fuzzy Soft Set and its Distance Measures
DOI:
https://doi.org/10.32350/sir.62.04Keywords:
cubic set, cubic intuitionistic fuzzy soft set, fuzzy set, intuitionistic set, intuitionistic fuzzy soft setAbstract
To deal with vagueness, falsity, attributive values, and inconsistency, this study introduced the cubic intuitionistic fuzzy soft set (CIFS-set) which is the extension of the cubic intuitionistic fuzzy set and proposed a distance measure, Hamming distance, Euclidean distance, and separation measures of CIFS-set. Moreover, we presented the aggregate operator (P-union, R-intersection) of CIFS-sets. The proposed CIFS-set is more reliable, efficient, and accurate. For the future research MCDM and MCGDM techniques could be proposed to deal with real-life issues, and this CIFS-set can also be extended for its hybrids.
Downloads
References
Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh (pp. 394-432).
Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 1(1), 3-28.
Mamdani, E. H., &Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13.
Kickert, W. J. M., & Mamdani, E. H. (1993). Analysis of a fuzzy logic controller. In Readings in Fuzzy Sets for Intelligent Systems (pp. 290-297). Morgan Kaufmann.
Saqlain, M., Naz, K., Gaffar, K., & Jafar, M. N. (2019). Fuzzy Logic Controller. Scientific Inquiry and Review, 3(3), 16-29.
Atanassov, K. (2016). Intuitionistic fuzzy sets. International journal bioautomation, 20, 1.
Atanassov, K. T. (2012). On intuitionistic fuzzy sets theory (Vol. 283). Springer.
Ejegwa, P. A., Akowe, S. O., Otene, P. M., & Ikyule, J. M. (2014). An overview on intuitionistic fuzzy sets. Int. J. Sci. Technol. Res, 3(3), 142-145.
De, S. K., Biswas, R., & Roy, A. R. (2000). Some operations on intuitionistic fuzzy sets. Fuzzy sets and Systems, 114(3), 477-484.
Atanassov, K. T. (1999). Interval valued intuitionistic fuzzy sets. In Intuitionistic Fuzzy Sets (pp. 139-177). Physica, Heidelberg.
Zhang, Q. S., Jiang, S., Jia, B., & Luo, S. (2010). Some information measures for interval-valued intuitionistic fuzzy sets. Information sciences, 180(24), 5130-5145.
Nayagam, V. L. G., Muralikrishnan, S., &Sivaraman, G. (2011). Multi-criteria decision-making method based on interval-valued intuitionistic fuzzy sets. Expert Systems with Applications, 38(3), 1464-1467.
Jun, Y. B., Kim, C. S., & Yang, K. O. (2012). Cubic sets. Ann. Fuzzy Math. Inform, 4(1), 83-98.
Garg, H., & Kaur, G. (2019). Cubic Intuitionistic Fuzzy Sets and its Fundamental Properties. Journal of Multiple-Valued Logic & Soft Computing, 33(6).
Kaur, G., & Garg, H. (2018). Cubic intuitionistic fuzzy aggregation operators. International Journal for Uncertainty Quantification, 8(5).
Molodtsov, D. (1999). Soft set theory—first results. Computers & Mathematics with Applications, 37(4-5), 19-31.
Maji, P. K., Biswas, R. K., & Roy, A. (2001). Fuzzy soft sets.
Maji, P. K., Biswas, R., & Roy, A. R. (2001). Intuitionistic fuzzy soft sets. Journal of fuzzy mathematics, 9(3), 677-692.
