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Novel Similarity Measure for Pythagorean and Intuitionistic Fuzzy Sets
Shagun Bernwal, Manoj Sahni, Cristhian Mellado-Cid and Ernesto Leon Castro
Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS) offer robust frameworks for handling uncertainty and vagueness in decision-making. This paper presents a novel chi-square similarity measure designed specifically for IFS and PFS. Traditional similarity measures often struggle to adequately handle the complexities inherent in these fuzzy set models. Through various numerical discussions spanning diverse domains, we demonstrate the effectiveness and versatility of the proposed measure. The case studies conducted illustrate its superiority over existing similarity measures in capturing nuanced relationships within fuzzy sets, offering valuable insights for decision-makers operating in uncertain environments. This research significantly contributes to advancing similarity measures in fuzzy set theory, providing practical tools to navigate complex decision-making scenarios.
Keywords: Similarity measures, chi-square similarity, intuitionistic fuzzy sets, pythagorean fuzzy sets