Version 1
: Received: 3 January 2024 / Approved: 4 January 2024 / Online: 5 January 2024 (12:02:37 CET)
How to cite:
Wang, H.; Li, C. New Tanimoto Similarity and Distance Measures for Pythagorean Fuzzy Sets With Applications. Preprints2024, 2024010449. https://doi.org/10.20944/preprints202401.0449.v1
Wang, H.; Li, C. New Tanimoto Similarity and Distance Measures for Pythagorean Fuzzy Sets With Applications. Preprints 2024, 2024010449. https://doi.org/10.20944/preprints202401.0449.v1
Wang, H.; Li, C. New Tanimoto Similarity and Distance Measures for Pythagorean Fuzzy Sets With Applications. Preprints2024, 2024010449. https://doi.org/10.20944/preprints202401.0449.v1
APA Style
Wang, H., & Li, C. (2024). New Tanimoto Similarity and Distance Measures for Pythagorean Fuzzy Sets With Applications. Preprints. https://doi.org/10.20944/preprints202401.0449.v1
Chicago/Turabian Style
Wang, H. and Chenglong Li. 2024 "New Tanimoto Similarity and Distance Measures for Pythagorean Fuzzy Sets With Applications" Preprints. https://doi.org/10.20944/preprints202401.0449.v1
Abstract
Currently, Pythagorean fuzzy sets (PFSs) have been widely applied in various fields due to their substantial advantages in expressing and dealing with uncertainty. However, measuring the similarity and difference between PFSs effectively remains an unresolved issue. Inspired by Tanimoto similarity, we propose a novel set of similarity and distance measures for PFSs. We delve into the theoretical properties of the proposed measures and compare it with existing PFSs measures. Numerous numerical examples validate their rationality and effectiveness. Furthermore, our experimental findings suggest that in contrast to existing measures, the introduced measures successfully circumvent various counter-intuitive issues encountered by current measures, and yield more pronounced outcomes in the discrimination of different fuzzy sets. This enhances the uniqueness and superiority of our measures. Finally, we developed two decision models based on the proposed measures and validated their applicability in three applications.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
