关于二项式 OWA 函数的线性组合

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Silvia Bortot, Ricardo Alberto Marques Pereira
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引用次数: 0

摘要

我们考虑了 Calvo 和 De Baets(1998 年)在 Choquet 积分框架下提出的有序加权平均(OWA)函数的二项式分解。本文的目的是进一步研究二项式分解所涉及的 OWA 函数的两种表示形式之间的等价性:以阶次统计量表示的通常规范表示形式,以及以二项式 OWA 函数表示的二项式表示形式。我们将详细描述和讨论这两种等价表示的系数之间的线性变换:以二项式表示的系数表示权重的原始表达式,以及以权重表示这些系数的逆表达式。在这两种情况下,我们都给出了简单直接的证明。此外,我们还在二项式 OWA 函数无约束线性组合的一般线性代数框架中考虑了两种表示法之间的线性变换。从这个角度出发,我们得到了线性变换的紧凑矩阵表达式,这也为 OWA 函数二项式表示中的系数约束几何提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On linear combinations of binomial OWA functions

We consider the binomial decomposition of ordered weighted averaging (OWA) functions proposed by Calvo and De Baets (1998) in the framework of Choquet integration. Our aim in the paper is to further investigate the equivalence between the two representations of OWA functions involved in the binomial decomposition: the usual canonical representation in terms of the order statistics, and the binomial representation in terms of the binomial OWA functions. We describe and discuss in detail the linear transformations that relate the coefficients of these two equivalent representations: the original expression of the weights in terms of the coefficients of the binomial representation, and its inverse, the expression of those coefficients in terms of the weights. In both cases simple and direct proofs are presented. Moreover, we consider the linear transformations between the two representations in the general linear algebra framework of unconstrained linear combinations of binomial OWA functions. In this perspective we obtain compact matrix expressions for the linear transformations, which also offer new insight on the geometry of the coefficient constraints in the binomial representation of OWA functions.

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来源期刊
Fuzzy Sets and Systems
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.
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