Bipolar Dissimilarity and Similarity Correlations of Numbers
Many papers on fuzzy risk analysis calculate the similarity between fuzzy numbers. Usually, they use symmetric and reflexive similarity measures between parameters of fuzzy sets or “centers of gravity” of generalized fuzzy numbers represented by real numbers. This paper studies bipolar similarity fu...
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| Format: | Article |
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MDPI AG
2022-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/5/797 |
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| author | Ildar Z. Batyrshin Edit Tóth-Laufer |
| author_facet | Ildar Z. Batyrshin Edit Tóth-Laufer |
| author_sort | Ildar Z. Batyrshin |
| collection | DOAJ |
| description | Many papers on fuzzy risk analysis calculate the similarity between fuzzy numbers. Usually, they use symmetric and reflexive similarity measures between parameters of fuzzy sets or “centers of gravity” of generalized fuzzy numbers represented by real numbers. This paper studies bipolar similarity functions (fuzzy relations) defined on a domain with involutive (negation) operation. The bipolarity property reflects a structure of the domain with involutive operation, and bipolar similarity functions are more suitable for calculating a similarity between elements of such domain. On the set of real numbers, similarity measures should take into account symmetry between positive and negative numbers given by involutive negation of numbers. Another reason to consider bipolar similarity functions is that these functions define measures of correlation (association) between elements of the domain. The paper gives a short introduction to the theory of correlation functions defined on sets with an involutive operation. It shows that the dissimilarity function generating Pearson’s correlation coefficient is bipolar. Further, it proposes new normalized similarity and dissimilarity functions on the set of real numbers. It shows that non-bipolar similarity functions have drawbacks in comparison with bipolar similarity functions. For this reason, bipolar similarity measures can be recommended for use in fuzzy risk analysis. Finally, the correlation functions between numbers corresponding to bipolar similarity functions are proposed. |
| first_indexed | 2024-03-09T20:31:39Z |
| format | Article |
| id | doaj.art-f830a24b422a4c169e9c2196e6671071 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T20:31:39Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-f830a24b422a4c169e9c2196e66710712023-11-23T23:23:47ZengMDPI AGMathematics2227-73902022-03-0110579710.3390/math10050797Bipolar Dissimilarity and Similarity Correlations of NumbersIldar Z. Batyrshin0Edit Tóth-Laufer1Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City 07738, MexicoBánki Donát Faculty of Mechanical and Safety Engineering, Óbuda University, Bécsi str 96/b, H-1034 Budapest, HungaryMany papers on fuzzy risk analysis calculate the similarity between fuzzy numbers. Usually, they use symmetric and reflexive similarity measures between parameters of fuzzy sets or “centers of gravity” of generalized fuzzy numbers represented by real numbers. This paper studies bipolar similarity functions (fuzzy relations) defined on a domain with involutive (negation) operation. The bipolarity property reflects a structure of the domain with involutive operation, and bipolar similarity functions are more suitable for calculating a similarity between elements of such domain. On the set of real numbers, similarity measures should take into account symmetry between positive and negative numbers given by involutive negation of numbers. Another reason to consider bipolar similarity functions is that these functions define measures of correlation (association) between elements of the domain. The paper gives a short introduction to the theory of correlation functions defined on sets with an involutive operation. It shows that the dissimilarity function generating Pearson’s correlation coefficient is bipolar. Further, it proposes new normalized similarity and dissimilarity functions on the set of real numbers. It shows that non-bipolar similarity functions have drawbacks in comparison with bipolar similarity functions. For this reason, bipolar similarity measures can be recommended for use in fuzzy risk analysis. Finally, the correlation functions between numbers corresponding to bipolar similarity functions are proposed.https://www.mdpi.com/2227-7390/10/5/797similarityfuzzy relationcorrelation between numbersbipolarityfuzzy risk assessment model |
| spellingShingle | Ildar Z. Batyrshin Edit Tóth-Laufer Bipolar Dissimilarity and Similarity Correlations of Numbers Mathematics similarity fuzzy relation correlation between numbers bipolarity fuzzy risk assessment model |
| title | Bipolar Dissimilarity and Similarity Correlations of Numbers |
| title_full | Bipolar Dissimilarity and Similarity Correlations of Numbers |
| title_fullStr | Bipolar Dissimilarity and Similarity Correlations of Numbers |
| title_full_unstemmed | Bipolar Dissimilarity and Similarity Correlations of Numbers |
| title_short | Bipolar Dissimilarity and Similarity Correlations of Numbers |
| title_sort | bipolar dissimilarity and similarity correlations of numbers |
| topic | similarity fuzzy relation correlation between numbers bipolarity fuzzy risk assessment model |
| url | https://www.mdpi.com/2227-7390/10/5/797 |
| work_keys_str_mv | AT ildarzbatyrshin bipolardissimilarityandsimilaritycorrelationsofnumbers AT edittothlaufer bipolardissimilarityandsimilaritycorrelationsofnumbers |