The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Pythagorean Fuzzy Information Measures and Their Applications
Open accessAbstract
Pythagorean fuzzy set (PFS), originally proposed by Yager, is more capable than intuitionistic fuzzy set (IFS) to handle vagueness in the real world. The main purpose of this paper is to investigate the relationship between the distance measure, the similarity measure, the entropy, and the inclusion measure for PFSs. The primary goal of the study is to suggest the systematic transformation of information measures (distance measure, similarity measure, entropy, inclusion measure) for PFSs. For achieving this goal, some new formulae for information measures of PFSs are introduced. To show the efficiency of the proposed similarity measure, we apply it to pattern recognition, clustering analysis, and medical diagnosis. Some illustrative examples are given to support the findings and also demonstrate their practicality and effectiveness of similarity measure between PFSs.
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Cites
Zadeh, L. A. (1965) · Information and Control · doi:10.1016/s0019-9958(65)90241-x · Cited by 67,855
Atanassov, K. T. (1986) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(86)80034-3 · Cited by 16,465
The Concept of a Linguistic Variable and Its Application to Approximate Reasoning—I
Zadeh, L. A. (1975) · Information Sciences · doi:10.1016/0020-0255(75)90036-5 · Cited by 12,187
The concept of a linguistic variable and its application to approximate reasoning-III
Zadeh, L. A. (1975) · Information Sciences · doi:10.1016/0020-0255(75)90017-1 · Cited by 12,139
Molodtsov, D. (1999) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(99)00056-5 · Cited by 4,702
Pythagorean Membership Grades in Multicriteria Decision Making
Yager, R. R. (2014) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2013.2278989 · Cited by 2,847
Yager, R. R. (2013) · 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) · doi:10.1109/ifsa-nafips.2013.6608375 · Cited by 2,703
Intuitionistic Fuzzy Aggregation Operators
Xu, Z. (2007) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2006.890678 · Cited by 2,678
The Concept of a Linguistic Variable and Its Application to Approximate Reasoning—II
Zadeh, L. A. (1975) · Information Sciences · doi:10.1016/0020-0255(75)90046-8 · Cited by 2,555
Some geometric aggregation operators based on intuitionistic fuzzy sets
Xu, Z., & Yager, R. R. (2006) · International Journal of General Systems · doi:10.1080/03081070600574353 · Cited by 2,356
Cited by
Rani, P., Mishra, A. R., Pardasani, K. R., Mardani, A., Liao, H., & Streimikiene, D. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.117936 · Cited by 297
Divergence measure of Pythagorean fuzzy sets and its application in medical diagnosis
Xiao, F., & Ding, W. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.03.043 · Cited by 292
Ganie, A. H., Gheith, N. E. M., Al-Qudah, Y., Ganie, A. H., Sharma, S. K., Aqlan, A. M., & Khalaf, M. M. (2024) · IEEE Access · doi:10.1109/access.2023.3348780 · Cited by 16
Metadata
- Authors
- Peng, X., Yuan, H., & Yang, Y.
- Venue
- International Journal of Intelligent Systems
- Publisher
- Wiley
- Published
- 2017-02-20
- Volume
- 32
- Issue
- 10
- Pages
- 991-1029
- ISSN
- 0884-8173 (ISSN-L)
- OpenAlex
- W2589233777
- Cited by
- 377
- References
- 49
- FWCI
- 35.79
- Type
- journal article
- Language
- en
- Open access
- bronze
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