Non-normal fuzzy number analysis in various levels using centroid method for fuzzy optimization
Abstract In the present article, a level analysis has been improved for various types of fuzzy numbers. In spite of non-normal fuzzy number ranking with more parameters are difficult, this analysis gives a clear idea for the non-normal case. The rank value may vary for different levels of various fu...
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Revathi, M. [verfasserIn] |
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Englisch |
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2021 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 25(2021), 14 vom: 06. Mai, Seite 8957-8969 |
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volume:25 ; year:2021 ; number:14 ; day:06 ; month:05 ; pages:8957-8969 |
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10.1007/s00500-021-05794-2 |
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10.1007/s00500-021-05794-2 doi (DE-627)OLC2126141853 (DE-He213)s00500-021-05794-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Revathi, M. verfasserin aut Non-normal fuzzy number analysis in various levels using centroid method for fuzzy optimization 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In the present article, a level analysis has been improved for various types of fuzzy numbers. In spite of non-normal fuzzy number ranking with more parameters are difficult, this analysis gives a clear idea for the non-normal case. The rank value may vary for different levels of various fuzzy numbers. The authors of this study essentially deal with the ranking approach, which is suitable to analyze three different fuzzy numbers, namely TrapFN, HFN, and HDFN, in the entire possible levels. The varying rank value in the fuzzy numbers can be identified by using the centroid ranking approach. Finally, a comparative analysis is given to demonstrate the advantages of the proposed analysis for fuzzy numbers levels. It is shown that the variation in ranking values of TrapFN, HFN, and HDFN is computed in a more efficient way. Non-normal fuzzy numbers Centroid method Symmetric fuzzy number Heptagonal fuzzy number Hendecagonal fuzzy number Valliathal, M. aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 25(2021), 14 vom: 06. Mai, Seite 8957-8969 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:25 year:2021 number:14 day:06 month:05 pages:8957-8969 https://doi.org/10.1007/s00500-021-05794-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 25 2021 14 06 05 8957-8969 |
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10.1007/s00500-021-05794-2 doi (DE-627)OLC2126141853 (DE-He213)s00500-021-05794-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Revathi, M. verfasserin aut Non-normal fuzzy number analysis in various levels using centroid method for fuzzy optimization 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In the present article, a level analysis has been improved for various types of fuzzy numbers. In spite of non-normal fuzzy number ranking with more parameters are difficult, this analysis gives a clear idea for the non-normal case. The rank value may vary for different levels of various fuzzy numbers. The authors of this study essentially deal with the ranking approach, which is suitable to analyze three different fuzzy numbers, namely TrapFN, HFN, and HDFN, in the entire possible levels. The varying rank value in the fuzzy numbers can be identified by using the centroid ranking approach. Finally, a comparative analysis is given to demonstrate the advantages of the proposed analysis for fuzzy numbers levels. It is shown that the variation in ranking values of TrapFN, HFN, and HDFN is computed in a more efficient way. Non-normal fuzzy numbers Centroid method Symmetric fuzzy number Heptagonal fuzzy number Hendecagonal fuzzy number Valliathal, M. aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 25(2021), 14 vom: 06. Mai, Seite 8957-8969 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:25 year:2021 number:14 day:06 month:05 pages:8957-8969 https://doi.org/10.1007/s00500-021-05794-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 25 2021 14 06 05 8957-8969 |
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Abstract In the present article, a level analysis has been improved for various types of fuzzy numbers. In spite of non-normal fuzzy number ranking with more parameters are difficult, this analysis gives a clear idea for the non-normal case. The rank value may vary for different levels of various fuzzy numbers. The authors of this study essentially deal with the ranking approach, which is suitable to analyze three different fuzzy numbers, namely TrapFN, HFN, and HDFN, in the entire possible levels. The varying rank value in the fuzzy numbers can be identified by using the centroid ranking approach. Finally, a comparative analysis is given to demonstrate the advantages of the proposed analysis for fuzzy numbers levels. It is shown that the variation in ranking values of TrapFN, HFN, and HDFN is computed in a more efficient way. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract In the present article, a level analysis has been improved for various types of fuzzy numbers. In spite of non-normal fuzzy number ranking with more parameters are difficult, this analysis gives a clear idea for the non-normal case. The rank value may vary for different levels of various fuzzy numbers. The authors of this study essentially deal with the ranking approach, which is suitable to analyze three different fuzzy numbers, namely TrapFN, HFN, and HDFN, in the entire possible levels. The varying rank value in the fuzzy numbers can be identified by using the centroid ranking approach. Finally, a comparative analysis is given to demonstrate the advantages of the proposed analysis for fuzzy numbers levels. It is shown that the variation in ranking values of TrapFN, HFN, and HDFN is computed in a more efficient way. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract In the present article, a level analysis has been improved for various types of fuzzy numbers. In spite of non-normal fuzzy number ranking with more parameters are difficult, this analysis gives a clear idea for the non-normal case. The rank value may vary for different levels of various fuzzy numbers. The authors of this study essentially deal with the ranking approach, which is suitable to analyze three different fuzzy numbers, namely TrapFN, HFN, and HDFN, in the entire possible levels. The varying rank value in the fuzzy numbers can be identified by using the centroid ranking approach. Finally, a comparative analysis is given to demonstrate the advantages of the proposed analysis for fuzzy numbers levels. It is shown that the variation in ranking values of TrapFN, HFN, and HDFN is computed in a more efficient way. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Valliathal, M. |
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231970536 |
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| doi_str |
10.1007/s00500-021-05794-2 |
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2024-07-04T06:04:07.287Z |
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1803627315633061888 |
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