GENERALIZED DIVERGENCE MEASURES IN INTUITIONISTIC FUZZY METRIC SPACES USING CONTINUOUS t-NORMS AND t-CONORMS

Abstract

This paper presents a novel class of generalized divergence measures for intu-itionistic fuzzy sets within the framework of intuitionistic fuzzy metric spaces. By incor-porating continuous t-norms and t-conorms, our approach extends the recent work of Liuet al. (2023) on aggregation operators for complex intuitionistic fuzzy sets. The proposedmeasures are formulated as exible, parameterized functions, thereby broadening the math-ematical foundation of divergence analysis and enhancing their applicability to real-worldfuzzy information systems. We rigorously establish key theoretical properties of these mea-sures, including non-negativity, symmetry, and convexity. In addition, we investigate theconvergence behavior of sequences of intuitionistic fuzzy sets, providing a unied frameworkfor analyzing similarity and dissimilarity in intuitionistic fuzzy systems. The generalizedmeasures encompass well-known divergences as special cases, oering new perspectives forapplications in multi-criteria decision analysis, machine learning, and signal processing.