随机排列集中的新正交和

IF 3.2 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems Pub Date : 2024-06-10 DOI:10.1016/j.fss.2024.109034

Yidan Wang , Zhen Li , Yong Deng

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引用次数: 0

摘要

随机排列集是一种新提出的处理不确定性的方法，它考虑了证据中元素的顺序。然而，如何有效地融合 RPS 仍是一个未决问题。为了解决这个问题，我们定义了由顺序码组成的空间。然后，还提出了该空间与置换事件空间之间的相互映射。最后，提出了新的正交和。与现有的左正交和相比，新正交和能以更低的熵获得更精确的结果，并能处理极端情况。计算实例说明了新正交和的优越性。

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A new orthogonal sum in Random Permutation Set

Random Permutation Set is a newly proposed method for handling uncertainty, which considers the order of the elements in evidence. However, how to fuse RPSs efficiently is still an open issue. To solve this problem, the space composed of order code is defined. Then the mutual mappings between this space and permutation event space are also presented. Finally, the new orthogonal sum is proposed. Compared with the existing left orthogonal sum, the new orthogonal sum can obtain more accurate results with lower entropy and deal with extreme situations. Numerical examples are used to illustrate the superiority of the new orthogonal sum.

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来源期刊

Fuzzy Sets and Systems 数学-计算机：理论方法

CiteScore

6.50

自引率

17.90%

发文量

321

审稿时长

6.1 months

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.