Version 1
: Received: 27 December 2023 / Approved: 28 December 2023 / Online: 29 December 2023 (11:56:34 CET)
How to cite:
Wang, H.; Tuo, C.; Feng, G.; Li, C. Fermatean Fuzzy Versions of Tanimoto Similarity Measure and Distance Measure and Their Application to Decision-Making Processes. Preprints2023, 2023122216. https://doi.org/10.20944/preprints202312.2216.v1
Wang, H.; Tuo, C.; Feng, G.; Li, C. Fermatean Fuzzy Versions of Tanimoto Similarity Measure and Distance Measure and Their Application to Decision-Making Processes. Preprints 2023, 2023122216. https://doi.org/10.20944/preprints202312.2216.v1
Wang, H.; Tuo, C.; Feng, G.; Li, C. Fermatean Fuzzy Versions of Tanimoto Similarity Measure and Distance Measure and Their Application to Decision-Making Processes. Preprints2023, 2023122216. https://doi.org/10.20944/preprints202312.2216.v1
APA Style
Wang, H., Tuo, C., Feng, G., & Li, C. (2023). Fermatean Fuzzy Versions of Tanimoto Similarity Measure and Distance Measure and Their Application to Decision-Making Processes. Preprints. https://doi.org/10.20944/preprints202312.2216.v1
Chicago/Turabian Style
Wang, H., Guoye Feng and Chenglong Li. 2023 "Fermatean Fuzzy Versions of Tanimoto Similarity Measure and Distance Measure and Their Application to Decision-Making Processes" Preprints. https://doi.org/10.20944/preprints202312.2216.v1
Abstract
Fermatean fuzzy sets (FFSs) are a versatile tool for handling uncertain problems and have shown great effectiveness in practical applications. However, many existing Fermatean fuzzy similarity measures exhibit counter-intuitive situations, making it challenging to accurately measure the similarity or difference between FFSs. To address this issue, several similarity and distance measures for FFSs are proposed, inspired by the Tanimoto similarity measure. The properties of the proposed measures are also explored, along with several comparative examples with existing measures for FFSs, which illustrate their superior performance in processing fuzzy information from FFSs. The proposed measures have effectively overcome the counter-intuitive challenges posed by many existing measures and significantly outperforms existing measures in differentiating different FFSs. Furthermore, we demonstrate the practical applicability of the proposed measures in solving pattern recognition, medical diagnosis and multiple-attribute decision-making problems.
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.
