A Jensen-a-Norm Dissimilarity Measure for Intuitionistic Fuzzy Sets and Its Applications in Multiple Attribute Decision Making

摘要

In the present communication, we have proposed a new dissimilarity measure based on Jensen inequality and a-norm divergence measure proposed by Boekee and Lubbe. Besides proving some of its major properties, the performance of the proposed dissimilarity measure is genuinely compared with some other existing measures in the literature through an example based on pattern recognition. Further, a method to solve multiple attribute decision making (MADM) problems based on the proposed dissimilarity measure is given and its performance is compared with some other well-known MADM methods.