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Soft Computing

Erschienen in:

06.05.2021 | Methodologies and Application

Non-normal fuzzy number analysis in various levels using centroid method for fuzzy optimization

verfasst von: M. Revathi, M. Valliathal

Erschienen in: Soft Computing | Ausgabe 14/2021

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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.

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Titel: Non-normal fuzzy number analysis in various levels using centroid method for fuzzy optimization
verfasst von: M. Revathi
M. Valliathal
Publikationsdatum: 06.05.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 14/2021
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-021-05794-2

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