专一性的语言学方法

C. Butler, J. Yen
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引用次数: 0

摘要

了解与预测相关的不确定性水平对于确定其在决策中的效用是有价值的。信息的一个衡量标准是R.R. Yager(1982)的特异性概念。Yager将特异性视为可能性分布指向话语域(U)中单个元素的程度。与U相关的特异性可能会使其在语言空间中模糊模型优化中的效用复杂化。粒度的增加对于衡量与模糊集相对的可能性分布中包含的信息量是有用的。这种抽象的特异性观点激发了对更广义的特异性版本的需求,称为语言特异性(Sp/sub L/),其中替代方案是根据语言术语来衡量的。这种泛化在支持模糊领域的自动化决策方面是有用的。语言特异性度量的结果用一个汽车油耗实例来说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A linguistic approach to specificity
Understanding the level of uncertainty associated with a prediction is valuable in determining its utility in decision making. One measure of information is R.R. Yager's (1982) notion of specificity. Yager views specificity as the degree to which a possibility distribution points to a single element in the universe of discourse (U). Specificity in relation to U may complicate its utility in the optimization of fuzzy models in their linguistic space. An increase in granularity is useful to measure the amount of information contained in a possibility distribution in relation to fuzzy sets as opposed to U. This abstracted view of specificity motivates the need for a more generalized version of specificity, denoted linguistic specificity (Sp/sub L/), where alternatives are measured in relation to the linguistic terms. Such a generalization is useful in support of automating decisions in a fuzzy domain. Results of the linguistic specificity measure are illustrated using an automobile fuel consumption example.
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