Similarity Measures for New Hybrid Models: mF Sets and mF Soft Sets

Abstract

Similarity measure for fuzzy systems plays a very substantial role in handling problems that contain uncertain information, but unable to deal the vagueness and uncertainty of the problems having multipolar information. In this research article, we define certain distances between two m-polar fuzzy sets (mF sets) and m-polar fuzzy soft sets (mF soft sets). We also propose a new similarity measure (SM) for mF sets and mF soft sets based on the distances. We demonstrate with an application that the proposed SM for mF sets is capable of recognizing different patterns. Moreover, we apply the concept of SM of mF soft sets to medical diagnosis. Finally, we summarize our proposed method as an algorithm in each application.