Picture Fuzzy Relations over Picture Fuzzy Sets
- 1 Department of Mathematics, Jahangirnagar University, Savar, Bangladesh
- 2 Department of Mathematics, Jahangirnagar University, Savar, Bangladesh
- 3 Department of Mathematics and Statistics, Bangladesh University of Business and Technology, Dhaka, Bangladesh
Abstract
Nowadays, picture fuzzy set theory is a flourishing field in mathematics with uncertainty by incorporating the concept of positive, negative and neutral membership degrees of an object. A traditional crisp relation represents the satisfaction or the dissatisfaction of relationship, connection or correspondence between the objects of two or more sets. However, there are some problems that can’t be solved through classical relationships, such as the relationship between two objects being vague. In those situations, picture fuzzy relation over picture fuzzy sets is an important and powerful concept which is suitable for describing correspondences between two vague objects. It represents the strength of association of the elements of picture fuzzy sets. It plays an important role in picture fuzzy modeling, inference and control system and also has important applications in relational databases, approximate reasoning, preference modeling, medical diagnosis , etc. In this article, we define picture fuzzy relations over picture fuzzy sets, including some other fundamental definitions with illustrations. The max-min and min-max compositions of picture fuzzy relations are defined in the light of picture fuzzy sets and discussed some properties related to them. The reflexivity, symmetry and transitivity of a picture fuzzy relation are described over a picture fuzzy set. Finally, various properties are explored related to the picture fuzzy relations over a picture fuzzy set.
- Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
- Atanassov, K.T. (1986) Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 20, 87-96. https://doi.org/10.1016/S0165-0114(86)80034-3
- Cuong, B.C. (2014) Picture Fuzzy Sets. Journal of Computer Science and Cybernetics, 30, 409-420.
- Zadeh, L.A. (1971) Similarity Relations and Fuzzy Orderings. Information Sciences, 3, 177-200. https://doi.org/10.1016/S0020-0255(71)80005-1
- Kaufman, A. (1975) Introduction to the Theory of Fuzzy Subsets: Fundamental Theoretical Elements. Academic Press, New York.
- Klir, G. and Yaun, B. (1995) Fuzzy Set and Fuzzy Logic: Theory and Application. Prentice Hall, Upper Saddle River.
- Zimmerman, H.J. (1996) Fuzzy Set Theory and Its Application. Kluwer Academic Publishers, Netherlands.
- Blin, J.M. (1974) Fuzzy Relations in Group Decision Theory. Journal of Cybernetics, 4, 17-22. https://doi.org/10.1080/01969727408546063
- Cock, M.D. and Kerre, E.E. (2003) On (Un)Suitable Fuzzy Relations to Model Approximate Equality. Fuzzy Sets and Systems, 133, 137-153. https://doi.org/10.1016/S0165-0114(02)00239-7
- Yang, M.S. and Shih, H.-M. (2001) Cluster Analysis Based on Fuzzy Relations. Fuzzy Sets and Systems, 120, 197-212. https://doi.org/10.1016/S0165-0114(99)00146-3
- Tamura, S., Higuchi, S. and Tanaka, K. (1971) Pattern Classification Based on Fuzzy Relations. IEEE Transactions on Systems, Man, and Cybernetics, SMC-1, 61-66. https://doi.org/10.1109/TSMC.1971.5408605
- Qi, F., Yang, S.W., Feng, X. and Jiang, X.L. (2013) Research on the Comprehensive Evaluation of Sports Management System with Interval-Valued Intuitionistic Fuzzy Information. Bulletin of Science and Technology, 29, 85-87.
- Jin, J.L., Wei, Y.M. and Ding, J. (2004) Fuzzy Comprehensive Evaluation Model Based on Improved Analytic Hierarchy Process. Journal of Hydraulic Engineering, 3, 65-70.
- Dai, W.Y., Zhou, C.M. and Lei, Y.J. (2009) Information Security Evaluation Based on Multilevel Intuitionistic Fuzzy Comprehensive Method. Microelectron Computer, 26, 75-179.
- Bustince, H. (2000) Construction of Intuitionistic Fuzzy Relations with Predetermined Properties. Fuzzy Sets and Systems, 109, 379-403. https://doi.org/10.1016/S0165-0114(97)00381-3