由一元函数和代数结构得到的几类模糊蕴涵的生成方法

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Isabel Aguiló , Vikash Kumar Gupta , Balasubramaniam Jayaram , Sebastia Massanet , Juan Vicente Riera , Nageswara Rao Vemuri
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引用次数: 0

摘要

现有的模糊蕴涵生成方法在所有模糊蕴涵集合中都是封闭的,但在应用于这些算子的某些族时却并非如此。本文在一些成熟的模糊蕴涵族上定义了一些二进制运算。也就是说,本文考虑了具有连续 t 准则的 (S,N)- 寓意族、从连续 t 准则得到的 R - 寓意族、雅格的 f - 和 g - 生成的寓意族、h - 和广义 (h,e)- 寓意族以及 k - 寓意族,并证明了通过这些运算,它们都是网格。此外,如果对蕴涵的底层生成器施加一些限制,还可以在某些族中定义一些其他的网格子结构。此外,我们还强调,这些生成方法保留了交换原则和导入法则等重要性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generating methods of some classes of fuzzy implications obtained by unary functions and algebraic structures

The existing generating methods of fuzzy implications are closed in the set of all fuzzy implications, but not when applied to some families of these operators. In this paper, some binary operations are defined on some well-established families of fuzzy implications. Namely, the families of (S,N)-implications with continuous t-conorms, R-implications obtained from continuous t-norms, Yager's f- and g-generated implications, h- and generalized (h,e)-implications and k-implications are considered and it is proved that with these operations, they are lattices. Moreover, some other lattice substructures are defined in some of the families when some restrictions on the underlying generators of the implications are imposed. Furthermore, it is emphasized that these generating methods preserve important properties like the exchange principle and the law of importation.

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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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