2014
DOI: 10.1186/s40467-014-0024-2
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New parametric generalized exponential fuzzy divergence measure

Abstract: The present paper introduces a parametric generalized exponential measure of fuzzy divergence of order α with the proof of its validity. A particular case of proposed fuzzy divergence measure is studied. Some properties of the new divergence measure between different fuzzy sets are proved. We establish a relation between exponential fuzzy entropy of order α and our fuzzy divergence measure. Further, a numerical example is given for the comparative study of the new divergence measure with some of existing measu… Show more

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Cited by 20 publications
(4 citation statements)
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“…Bhatia and Singh [4] developed four fuzzy divergence measures. Tomar and Ohlan [18] introduced a generalised divergence measure based on exponential distribution. Verma and Sharma [19] introduced a generalised relative information measure for intuitionistic fuzzy sets.…”
Section: Introductionmentioning
confidence: 99%
“…Bhatia and Singh [4] developed four fuzzy divergence measures. Tomar and Ohlan [18] introduced a generalised divergence measure based on exponential distribution. Verma and Sharma [19] introduced a generalised relative information measure for intuitionistic fuzzy sets.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, more similarity measures can be derived from distance measures and vice versa. Furthermore, the numerous authors (Arora & Tomar, 2020;Dass et al, 2019;Tomar, 2019;Tomar & Ohlan, 2014) have developed the entropy and discrimination measures related to FSs and IFSs. Initially, IFSs distance measures were conceptualised by Szmidt & Kacprzyk (2000).…”
Section: Introductionmentioning
confidence: 99%
“…The essential reason is to evaluate how much data is contained within the information. Now a days, these measures are being connected in a few disciplines such as: color picture division [17], estimation of likelihood dispersions [4,8], design acknowledgment [9,23], 3D picture division and word arrangement [20], choice making [16,22,24,25], attractive reverberation picture investigation [27], fetched-touchy classification for therapeutic conclusion [19], turbulence stream [5], fuzzy divergence and applications [3,10,15,21,26], etc. Let Θ l = {U = (u 1 , u 2 , u 3 , ..., u l ) : u i > 0, l i=1 u i = 1}, l ≥ 2 be the set of all complete finite discrete probability distributions, where u i is a probability mass function.…”
Section: Introductionmentioning
confidence: 99%