A Family of New Distance Models for Discrete Fuzzy Distributions and their detailed Properties

Abstract

The distance measures are incredibly imperative and play a fundamental responsibility towards optimization problems in discrete probability spaces. But, where probabilistic measures do not work, one can travel around the possibility of divergence measures in fuzzy distributions. This communiqué has been advocated for the generation of a family of original divergence models for discrete fuzzy distributions corresponding to the existing probabilistic measures and studied their comprehensive properties for proving their legitimacy.