关于有界格子上的制服类Umin和Umax

IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ya-Ming Wang , Yexing Dan , Bernard De Baets
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引用次数: 0

摘要

最近,有界格上的一致信息的概念被推广到有界格上。格和格的根本区别在于格的伪序关系不一定是传递的。在本文中,我们从结构和表征的角度对有界格子上的某些类型的均匀进行了深入的解剖。我们首先在有界网格上引入了类Umin和类Umax的均匀信息,并为它们的表征提供了充分必要条件。随后,我们利用在有界网格的闭子区间上定义的一致项,给出了两种构造有界网格上一致项的方法。结果表明,这些方法不仅在有界格上得到与现有方法不同的一致信息,而且推广了在有界格的闭子区间上定义一致信息构造的有界格上的一致信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the classes of uninorms Umin and Umax on bounded trellises
Recently, the concept of a uninorm on a bounded lattice has been generalized to bounded trellises. A fundamental distinction between lattices and trellises lies in the fact that the underlying pseudo-order relation of a trellis does not need to be transitive. In this paper, we undertake an in-depth dissection of certain types of uninorms on bounded trellises, both from a construction and a characterization point of view. We begin by introducing the uninorms in the classes Umin and Umax on a bounded trellis and provide necessary and sufficient conditions for their characterization. Subsequently, we present two approaches for constructing uninorms on a bounded trellis by utilizing a uninorm defined on a closed subinterval of that bounded trellis. It is shown that these approaches yield uninorms on a bounded trellis that not only differ from the ones obtainable through existing methods, but also generalize those on a bounded lattice constructed via a uninorm defined on a closed subinterval of that bounded lattice.
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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