基于非分布网格的卷积网格特征描述

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Zhi-qiang Liu
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引用次数: 0

摘要

最近,De Miguel、Bustince 和 De Baets 对基于分布网格的卷积网格进行了系统研究。关于将非分布网格应用于函数域的报道还很少。作为对他们工作的补充,我们在本文中对非分布网格(域)和框架(共域)之间的函数卷积操作进行了深入研究。我们首先提出了非分布网格与幂等函数之间的等价性描述,并进一步证明了幂等函数集合的一个子集在卷积操作下是封闭的。我们证明了这个子集也是双线格,并且在连接卷积和相遇卷积操作下满足伯克霍夫方程。最后,我们分析并研究了与所得代数结构相关的网格结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characterizations for convolution lattices based on non-distributive lattices

Recently, De Miguel, Bustince and De Baets have conducted a systematic study on convolution lattices based on distributive lattices. There have been few reports on applying non-distributive lattices to a domain of functions. As a complement to their work, in this paper, we carry out an in-depth investigation of convolution operations of the functions between a non-distributive lattice (domain) and a frame (co-domain). We first present an equivalence characterization between non-distributive lattices and idempotent functions and further show that a subset of the set of idempotent functions is closed under convolution operations. We demonstrate that this subset also is a bisemilattice and satisfies the Birkhoff equation under join- and meet-convolution operations. Finally, we analyze and study the lattice structure related to the obtained algebraic structure.

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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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