Pythagorean Fuzzy Information Measures and Their Applications
By Xindong Peng, Huiyong Yuan, Yong Yang. International Journal of Intelligent Systems, 2017
332 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/3393af74e4398f6b3de5028a781f9653cecb3d1d
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In Computational Theory and Mathematics
In Management Science and Operations Research
Citations in the corpus
- Pythagorean fuzzy set: state of the art and future directions (2017, methodology): “Meanwhile, it also has widely used in different environment, such as neutrosophic set (Peng and Dai 2016), interval-valued intuitionistic fuzzy set (Xue et al. 2016), interval-valued fuzzy soft set (Peng et al. 2017a) and interval type-2 fuzzy set (Yu et al. 2017).”