论文标题
具有聚合功能的模糊推断的MP和MT特性
MP and MT properties of fuzzy inference with aggregation function
论文作者
论文摘要
作为两个基本模糊的推理模型，模糊的作案PONEN（FMP）和模糊Modus Tollens（FMT）具有重要的应用在人工智能中。为了解决FMP和FMT问题，Zadeh提出了一种组成的推理规则（CRI）方法。本文旨在分别从逻辑观点和插值视图中研究基于聚合函数的广义CRI方法，主要研究推理A-复合法则（ACRI）方法的有效性。具体而言，详细讨论了ACRI方法的Modus Ponens（MP）和Modus Tollens（MT）属性。结果表明，实施FMP和FMT问题的聚合功能比T-norms，Unnomorms和重叠函数提供了更多的通用性，分别是T条件，U条件性和O条件的定律。此外，还举了两个示例来说明我们的理论结果。尤其是，示例6.2表明，当FMP（FMT）问题中的输出B'与我们所提出的推理方法接近B（DC），当模糊输入和模糊规则的先行性靠近时（模糊输入与模糊规则的否定为否定的模糊输入）。
As the two basic fuzzy inference models, fuzzy modus ponens (FMP) and fuzzy modus tollens (FMT) have the important application in artificial intelligence. In order to solve FMP and FMT problems, Zadeh proposed a compositional rule of inference (CRI) method. This paper aims mainly to investigate the validity of A-compositional rule of inference (ACRI) method, as a generalized CRI method based on aggregation functions, from a logical view and an interpolative view, respectively. Specifically, the modus ponens (MP) and modus tollens (MT) properties of ACRI method are discussed in detail. It is shown that the aggregation functions to implement FMP and FMT problems provide more generality than the t-norms, uninorms and overlap functions as well-known the laws of T-conditionality, U-conditionality and O-conditionality, respectively. Moreover, two examples are also given to illustrate our theoretical results. Especially, Example 6.2 shows that the output B' in FMP(FMT) problem is close to B(DC) with our proposed inference method when the fuzzy input and the antecedent of fuzzy rule are near (the fuzzy input near with the negation of the seccedent in fuzzy rule).
