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Ranking of intuitionistic fuzzy numbers

Authors: V. Lakshmana Gomathi Nayagam; G. Venkateshwari; Geetha Sivaraman;

Ranking of intuitionistic fuzzy numbers

Abstract

The notion of fuzzy subsets was introduced by L.A.Zadeh (1965) and it was generalised to intuitionistic fuzzy subsets by K.Atanassov [1]. After the invention of intuitionistic fuzzy subsets, many real life problems are studied accurately [7, 13, 14]. The measure of fuzziness was studied in [12, 16]. The ranking of intuitionistic fuzzy numbers plays a main role in modelling many real life problems involving intuitionistic fuzzy decision making, intuitionistic fuzzy clustering. H.B.Mitchell introduced a method of ranking intuitionistic fuzzy numbers in [10]. In this paper, a new method of intuitionistic fuzzy scoring to intuitionistic fuzzy numbers that generalizes Chen and Hwangpsilas scoring method for ranking of intuitionistic fuzzy numbers has been introduced and studied.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
19
Top 10%
Top 10%
Average
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