Abstract
Vagueness is an integral part of different perceptions around the world. Entropy underlies the evaluation of questionable data first presented by Shannon [1] to indicate the degree of skepticism in the probable spread. Fuzzy data measures are widely used in decision-making areas. The Jensen-Shannon difference is a useful measure of distance in the space of probability distribution. In this present correspondence we have a method for estimating the difference between two spectral sets. Investigations of their comprehensive properties for their legitimacy are also mentioned. The numerical controls are listed, which illustrate the proposed technique for solving large decision making problems in fuzzy conditions.
Highlights
Information theory advanced out of mathematical studies of the problems linked with communication, storage, and transmission of massages
We have proposed a relative distance measure for fuzzy sets
Proof of its validity is considered through numerical computations
Summary
Information theory advanced out of mathematical studies of the problems linked with communication, storage, and transmission of massages. To explain the distinction among fuzzy sets, the distance measure was set up and was regarded as dual model of correspondence measure Many researchers, such as Yager [12], Kosko [13] and Kaufmann [6] had used distance measure to define fuzzy entropy. Shang and Jiang [22] was pointed out that the expression (4) has some limitations, i.e., if μA(xi) approaches to 0 or 1, its value tends to ∞ They proposed a modified version of fuzzy divergence measure (4), given as between fuzzy sets and we study in detail the case of local divergence. Verma and Shrama [24] define a measure of inaccuracy of fuzzy set B with respect to fuzzy set A, as
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