满足 I(r,I(s,t))=I(I(r,s),I(r,t))的(U,N)-应用的新成果

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Approximate Reasoning Pub Date : 2024-03-12 DOI:10.1016/j.ijar.2024.109163
Cheng Zhang , Feng Qin
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引用次数: 0

摘要

在模糊数学领域,特别是在模糊逻辑框架内,众多学者对广义弗雷格定律进行了广泛的探索。本研究旨在进一步研究满足该定律的-蕴涵,并提出了许多新发现。首先,为了有效地确定广义弗雷格定律对任何-蕴涵的可满足性,我们引入了两个简单实用的新必要条件:对于模糊否定 ,它必须是非连续的,而且它在区间内的值应该保持常数 1。接下来,我们提供了任何 - 含义满足广义弗雷格定律的必要条件和充分条件。根据......中的位置,描述了几种完整的特征。更具体地说,当()和一个具有连续底层 t 准则(t 准则)的析取非矩形时,就能实现完整的特征描述。当 和 是局部内部析取非矩形时,提出了必要条件和充分条件。
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New results of (U,N)-implications satisfying I(r,I(s,t))=I(I(r,s),I(r,t))

Generalized Frege's law has been extensively explored by numerous scholars in the field of fuzzy mathematics, particularly within the framework of fuzzy logic. This study aims to further investigate the (U,N)-implications that satisfy this law and presents a multitude of novel findings. First, to efficiently determine the satisfiability of the generalized Frege's law for any (U,N)-implication, two new necessary conditions have been introduced that are simple and practical: for the fuzzy negation N, it must be noncontinuous, and its values in the interval [0,e] should remain the constant 1. Next, the necessary and sufficient conditions for any (U,N)-implication to satisfy the generalized Frege's law are provided. Several complete characterizations are described depending on the position of α in [e,1]. To be more specific, the full characterization is achieved when α=e (α=1) and a disjunctive uninorm with a continuous underlying t-norm (t-conorm). The necessary and sufficient conditions are presented when α]e,1[ and U is a locally internal and disjunctive uninorm.

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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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