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The likelihood interpretation as the foundation of fuzzy set theory

机译:似然解释是模糊集理论的基础

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In order to use fuzzy sets in real-world applications, an interpretation for the values of membership functions is needed. The history of fuzzy set theory shows that the interpretation in terms of statistical likelihood is very natural, although the connection between likelihood and probability can be misleading. In this paper, the likelihood interpretation of fuzzy sets is reviewed: it makes fuzzy data and fuzzy inferences perfectly compatible with standard statistical analyses, and sheds some light on the central role played by extension principle and a-cuts in fuzzy set theory. Furthermore, the likelihood interpretation justifies some of the combination rules of fuzzy set theory, including the product and minimum rules for the conjunction of fuzzy sets, as well as the probabilistic sum and bounded-sum rules for the disjunction of fuzzy sets. (C) 2017 Elsevier Inc. All rights reserved.
机译:为了在实际应用中使用模糊集,需要对隶属函数的值进行解释。模糊集理论的历史表明,尽管似然率和概率之间的联系可能会产生误导,但根据统计似然率的解释是很自然的。本文对模糊集的似然解释进行了综述:它使模糊数据和模糊推理与标准统计分析完全兼容,并阐明了扩展原理和a-割在模糊集理论中的核心作用。此外,似然解释证明了模糊集理论的一些组合规则,包括用于模糊集结合的乘积和最小规则,以及用于模糊集分离的概率和和有界和规则。 (C)2017 Elsevier Inc.保留所有权利。

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