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.
Muhammad Akram, Neha Waseem. (2019) Similarity Measures for New Hybrid Models: mF Sets and mF Soft Sets, Punjab University Journal of Mathematics, Volume 51, Issue 6.
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