基于偏好暗示的近似推理

IF 3.2 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Fuzzy Sets and Systems Pub Date : 2024-11-07 DOI:10.1016/j.fss.2024.109187
József Dombi , Tamás Jónás
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引用次数: 0

摘要

在模糊逻辑中,大多数蕴涵运算符都是基于对经典的物质蕴涵的概括。也就是说,这些蕴涵被定义为第一个参数的否定值和第二个参数的值的析取,而基本的析取算子是关联三角形康式。在我们的研究中,我们将重点关注如何在近似推理中使用一类蕴涵算子,即偏好蕴涵算子。利用这个蕴涵算子族,我们提出了一种新颖的、类似于模态庞恩的近似推理方法,其中我们有两个前提:(1)一个语句和(2)一个带有该语句前因的偏好蕴涵。在这里,我们展示了如何从前提的连续逻辑值推导出偏好蕴涵的后果的连续逻辑值。我们指出,这种新颖的近似推理方法与所谓的聚合算子密切相关,而聚合算子是一种可表示的非规范。接下来,我们还介绍了基于阈值的莫度庞恩式三段论概括,并证明莫度托伦斯式三段论也可以用同样的方法概括。最后,我们提供了一个示例。
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Approximate reasoning based on the preference implication
In fuzzy logic, most of the implication operators are based on generalizations of the classical, material implication. That is, these implications are defined as the disjunction of the negated value of the first argument and the value of the second argument, while the underlying disjunction operators are associative triangular conorms. In our study, we concentrate on how a class of implication operators, called the preference implication operators, can be used in approximate reasoning. Using this implication operator family, we present a novel, Modus Ponens-like approximate reasoning method, in which we have two premises: (1) a statement and (2) a preference implication with an antecedent of this statement. Here, we show how the continuous logical value of the consequent of the preference implication can be derived from the continuous logical values of the premises. We point out that this novel approximate reasoning method is strongly connected with the so-called aggregative operator, which is a representable uninorm. Next, we also present a threshold value-based generalization of the Modus Ponens syllogism and demonstrate that the Modus Tollens syllogism can be generalized in the same way. Lastly, we provide an illustrative example.
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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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