Probability hesitation fuzzy set recognition method based on hybrid correlation coefficient

被引：0
|
作者：
Liu Y. [1 ]
Guan X. [1 ]
机构：
[1] Academy of Aviation Combat Service, Naval Aeronautical University, Yantai
来源：
Kongzhi yu Juece/Control and Decision | 2023年 / 38卷 / 07期
关键词：
correlation analysis; hesitation fuzzy set; length rate; mixed correlation coefficient; multi-attribute decision making; probabilistic hesitation fuzzy set;
D O I：
10.13195/j.kzyjc.2021.1985
中图分类号：
学科分类号：
摘要：
Aiming at the defects of the existing researches on the correlation coefficients of probabilistic hesitation fuzzy sets (PHFSs), such as not considering the number of membership degrees and having counterintuitive phenomena, a new hybrid correlation coefficient is proposed. The hybrid correlation coefficient can comprehensively reflect the individual and global correlation among the PHFSs, which is more comprehensive and reasonable than the existings. Firstly, three factors of integrity, distribution and length of elements in PHFEs are considered comprehensively, and three basic correlation coefficients of mean, variance and length rate are defined respectively. On this basis, the hybrid correlation coefficient is obtained, which is proved to meet the axiomatic definition criterion of correlation coefficient. The results of case analysis show that the hybrid correlation coefficient overcomes the defects of the existing PHFS correlation coefficient. Based on the hybrid correlation coefficient, the multi-attribute decision making method in the PHFS environment is further designed. Finally, the validity and rationality of the correlation coefficient proposed in this paper is verified by a specific case analysis. © 2023 Northeast University. All rights reserved.
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页码：2019 / 2027
页数：8
相关论文
共 25 条
  • [1] Fang B, Han B, Wen C H., Probabilistic hesitant fuzzy multi-attribute group decision-making based on new distance measure, Control and Decision, 37, 3, pp. 729-736, (2022)
  • [2] Liu Y M, Zhu F, Jin L L., Multi-attribute decision method based on probabilistic hesitant fuzzy entropy, Control and Decision, 34, 4, pp. 861-870, (2019)
  • [3] Zadeh L A., Fuzzy sets, Information and Control, 8, 3, pp. 338-353, (1965)
  • [4] Torra V., Hesitant fuzzy sets, International Journal of Intelligent Systems, 25, 6, pp. 529-539, (2010)
  • [5] Zhu B., Decision making methods and appplications based on preference relations, (2014)
  • [6] Zhang S, Xu Z S, He Y., Operations and integrations of probabilistic hesitant fuzzy information in decision making, Information Fusion, 38, pp. 1-11, (2017)
  • [7] Park J, Park Y, Son M., Hesitant probabilistic fuzzy information aggregation using Einstein operations, Information, 9, 9, (2018)
  • [8] Shao S T, Zhang X H, Zhao Q., Multi-attribute decision making based on probabilistic neutrosophic hesitant fuzzy choquet aggregation operators, Symmetry, 11, 5, (2019)
  • [9] Li J, Wang Z X., Multi-attribute decision making based on prioritized operators under probabilistic hesitant fuzzy environments, Soft Computing, 23, 11, pp. 3853-3868, (2019)
  • [10] Zhou W, Xu Z S., Group consistency and group decision making under uncertain probabilistic hesitant fuzzy preference environment, Information Sciences, 414, pp. 276-288, (2017)
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