Approximate reasoning in knowledge-based fuzzy sets

2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622) Pub Date : 2002-08-07 DOI:10.1109/NAFIPS.2002.1018100

R. Intan, M. Mukaidono

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引用次数: 7

Abstract

A fuzzy set is considered to represent deterministic uncertainty called fuzziness. In deterministic uncertainty of a fuzzy set, one may subjectively determine a membership function of a given element by one's knowledge. Different persons with different knowledge may provide different membership functions for elements in a universe with respect to a given fuzzy set. Here, knowledge plays important roles in determining or defining a fuzzy set. By adding a component of knowledge, we generalized a definition of a fuzzy set based on probability theory. In addition, by using a fuzzy conditional probability relation, granularity of knowledge is given in two frameworks, crisp granularity and fuzzy granularity. Also, two asymmetric similarity classes or subsets of knowledge are considered. When fuzzy sets represent problems or situations, a granule of knowledge might describe a class (group) of knowledge (persons) who has similar point of view in dealing the problems. In the paper, special attention is given to approximate reasoning in knowledge-based fuzzy sets representing fuzzy production rules as usually used in fuzzy expert systems.

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基于知识的模糊集近似推理

模糊集被认为代表确定性的不确定性，称为模糊性。在模糊集的确定性不确定性中，人们可以根据自己的知识主观地确定给定元素的隶属函数。对于给定的模糊集，具有不同知识的人可以为宇宙中的元素提供不同的隶属函数。在这里，知识在确定或定义模糊集方面起着重要的作用。在概率论的基础上，通过添加知识分量，推广了模糊集的定义。此外，利用模糊条件概率关系，将知识的粒度划分为清晰粒度和模糊粒度两种框架。此外，还考虑了两个不对称的相似类或知识子集。当模糊集表示问题或情况时，知识颗粒可以描述在处理问题时具有相似观点的一类(组)知识(人)。本文重点研究了模糊专家系统中常用的基于知识的模糊集近似推理问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622)

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