Yuan-Kai Hu , Fang Liu , Xin-Xing Deng , Jie-Yun Wang
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An algebraic framework for uniformly analyzing interval pairwise comparison matrices
This paper reports an algebraic framework for uniformly analyzing multiple types of interval pairwise comparison matrices (IPCMs). First, a possibility degree formula of ranking interval numbers is developed by using an isomorphic mapping. The partial order of intervals is established such that the equivalence classes can be constructed. Based on the binary operation and partial order of intervals, an Abelian linearly ordered (Alo) group is formed. Second, the reciprocal property and consistency of IPCMs are redefined within the framework of Alo group. It is found that the shortcoming of some existing consistency definitions of IPCMs is overcome. Third, a novel index vector is proposed to measure the inconsistency level of IPCMs. The partial order of the index vector is established for defining acceptable consistency of IPCMs. The results show that the isomorphisms between Alo groups offer a theoretical basis to uniformly investigate the properties of various IPCMs.
期刊介绍:
Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies.
In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.