X-mu方法:模糊量、模糊算法和模糊关联规则

T. Martin, B. Azvine
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引用次数: 21

摘要

使用所谓的模糊数进行近似计算会导致严重的问题,因为其潜在的数学结构比普通的算术要弱。许多这样的问题产生于这样一个事实,即模糊量实际上是模糊区间。渐进数最近被提出作为模糊量的更好的表示。本文描述了一种新的模糊量函数的可视化和计算方法——X-μ方法。特别地,我们说明了模糊关联置信度的计算,当隶属度可以用函数或值表表示时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The X-mu approach: Fuzzy quantities, fuzzy arithmetic and fuzzy association rules
The use of so-called fuzzy numbers for approximate calculations leads to significant problems, because the underlying mathematical structure is weaker than ordinary arithmetic. Many of these problems arise from the fact that the fuzzy quantities are actually fuzzy intervals. Gradual numbers were recently proposed as a better representation for fuzzy quantities. In this paper, we describe the X-μ approach, a new method of visualizing and calculating functions of fuzzy quantities. In particular, we illustrate the calculation of fuzzy association confidence in cases where membership can be represented by a function or a table of values.
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