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Towards an Operational Interpretation of Membership Grades - On H-Valued Fuzzy Sets and Their Use for Fuzzy Quantification

机译:走向会员等级的操作解释-关于H值模糊集及其在模糊量化中的应用

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Advances in fuzzy quantification have rendered possible a consistent interpretation of quantifying expressions involving vague quantifiers and fuzzy arguments (Glockner 2006, Diaz-Hermida et al 2005). However, the assumption of these approaches that the modeller is able to specify precise [0,1]-valued membership functions for the involved fuzzy sets and fuzzy quantifiers can be too strong in certain cases. To alleviate this problem, we extend the existing theory of fuzzy quantification to lattice-valued fuzzy sets which no longer require a specification of precise numerical membership grades. The paper focuses on a special type of so called H-lattices whose Hasse diagram has an hourglass shape. In this setting, we can achieve an operational interpretation of membership values, which can be calculated automatically provided that the modeller (a) decides on the basic tendency of the membership assessments and (b) specifies the salient ordering relationships between the confidence levels. The generalization of the existing theory of fuzzy quantification to H-valued fuzzy sets is a straightforward task and few properties of the models will be lost when turning from [0,1] to the generalized valuations. It is even possible to devise a generic construction which assigns a plausible model of fuzzy quantification to any given H-lattice
机译:模糊量化的进步使得对含模糊量词和模糊论证的量化表达式进行一致的解释成为可能(Glockner 2006,Diaz-Hermida等,2005)。但是,在某些情况下,这些方法的假设是建模者能够为所涉及的模糊集和模糊量词指定精确的[0,1]值隶属函数。为了缓解这个问题,我们将现有的模糊量化理论扩展到不再需要精确的数值隶属度等级的晶格值模糊集。本文重点研究一种特殊的所谓H格,其Hasse图具有沙漏形状。在这种情况下,我们可以实现隶属度值的操作解释,只要建模者(a)决定隶属度评估的基本趋势,并且(b)指定置信度之间的显着排序关系,就可以自动计算该值。将现有的模糊量化理论推广到H值模糊集是一项简单的任务,当从[0,1]转到广义评估时,模型的几乎所有属性都将丢失。甚至有可能设计出一种通用结构,该结构将模糊量化的合理模型分配给任何给定的H晶格

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