Power of Continuous Triangular Norms with Application to Intuitionistic Fuzzy Information Aggregation
Xinxing Wu, Xi Li, Dan Huang
TL;DR
This work advances the theory and application of power operations for continuous t-norms in intuitionistic fuzzy contexts. It characterizes power stability via a complete structural taxonomy (minimum, strict, ordinal sums) and provides a computable power formula through the representation theorem. Building on this, it defines four fundamental IF operations, develops IFWA and IFWG, and introduces IFMWAG, a robust operator for IFMAD. The resulting MADM method demonstrates improved discrimination and broader applicability across IF information with real-world relevance. Overall, the study extends IF aggregation to general continuous t-norms and offers a practical decision-making toolkit grounded in power-based operations.
Abstract
The power operation of continuous Archimedean triangular norms (t-norms) is fundamental for generalizing the multiplication and power operations of intuitionistic fuzzy sets (IFSs) within the framework of continuous Archimedean t-norms. However, due to the lack of systematic research on the power operation of general continuous t-norms in theory, it greatly limits the further generalization of the multiplication and power operations for IFSs via general continuous t-norms. This paper aims to investigate the power operation of continuous t-norms and develop some IF information aggregation methods. In theory, it is proved that a continuous t-norm is power stable if and only if every point is a power stable point, and if and only if it is the minimum t-norm, or it is strict, or it is an ordinal sum of strict t-norms. Moreover, the representation theorem of continuous t-norms is used to obtain the computational formula for the power of continuous t-norms. Based on the power operation of t-norms, four fundamental operations induced by a continuous t-norm for the IFSs are introduced. Furthermore, various IF aggregation operators based on these four fundamental operations, namely the IF weighted average (IFWA), IF weighted geometric (IFWG), and IF mean weighted average and geometric (IFMWAG) operators, are defined, and their properties are analyzed. In application, a new decision-making algorithm is designed based on the IFMWAG operator, which can remove the hindrance of indiscernibility on the boundaries of some classical aggregation operators. The practical applicability, comparative analysis, and advantages of the study with other decision-making methods are furnished to ascertain the efficacy of the designed method.