基于分组特征的模糊关系组合

N. Cao, M. Štěpnička, M. Burda, A. Dolný
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引用次数: 5

摘要

模糊关系组合在模糊数学的基础和不同的应用领域中起着至关重要的作用。最近的研究引入了不同的概括,例如,排除特征的结合或广义量词的使用。不管这些方法有多么巨大的积极潜力,我们在一个真实的例子中证明,如果以某种特定但非常自然的方式构造这些特性,即使是这些扩展也可能遇到一些限制。但是,如果应用某种特征分组,则可以通过进一步改进来克服这些限制。特别地,我们研究了基于划分特征域的模糊关系的组合,然后将上述两种扩展的组合应用并进行了实验验证。将所得结果与不使用广义量词和不使用特征分组的排除特征的研究结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy relational compositions based on grouping features
Fuzzy relational compositions play a crucial role in fundamentals of fuzzy mathematics as well as in distinct application areas. Recent studies introduce distinct generalizations, e.g., incorporation of excluding features or the use of generalized quantifiers. No matter the huge positive potential of these approaches, we demonstrate on a real example, that some limitations even for these extensions may be encountered if the features are constructed in a certain specific yet very natural way. However, these limitations can be overcome by a further improvement if a sort of grouping of features is applied. In particular, we study the compositions of fuzzy relations based on partitioned universe of features and then the combination of both above mentioned extensions will be applied and experimentally validated. The results are compared to the original ones provided in the investigation of excluding features without any implementation of generalized quantifiers nor with grouping of the features.
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