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Neural Computing and Applications
Published in: Neural Computing and Applications 8/2017

25-04-2016 | Original Article

A new measure of divergence with its application to multi-criteria decision making under fuzzy environment

Authors: Rajkumar Verma, Shikha Maheshwari

Published in: Neural Computing and Applications | Issue 8/2017

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Abstract

Divergence measure is an important tool for determining the amount of discrimination between two probability distributions. Since the introduction of fuzzy sets, divergence measures between two fuzzy sets have gained attention for their applications in various fields. Exponential entropy measure has some advantages over Shannon’s entropy. In this paper, we used the idea of Jensen–Shannon divergence to define a new divergence measure called ‘fuzzy Jensen-exponential divergence (FJSD)’ for measuring the discrimination/difference between two fuzzy sets. The measure is demonstrated to satisfy some very elegant properties, which shows its strength for applications in multi-criteria decision-making problems. Further, we develop a method to solve multi-criteria decision-making problems under fuzzy phenomenon by utilizing the proposed measure and demonstrate by a numerical example.
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Metadata
Title
A new measure of divergence with its application to multi-criteria decision making under fuzzy environment
Authors
Rajkumar Verma
Shikha Maheshwari
Publication date
25-04-2016
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2017
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2311-y

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