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A New Similarity Measure between Intuitionistic Fuzzy Sets and Its Application to Pattern Recognition

机译:直觉模糊集之间的一种新的相似性度量及其在模式识别中的应用

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As a generation of ordinary fuzzy set, the concept of intuitionistic fuzzy set (IFS), characterized both by a membership degree and by a nonmembership degree, is a more flexible way to cope with the uncertainty. Similarity measures of intuitionistic fuzzy sets are used to indicate the similarity degree between intuitionistic fuzzy sets. Although many similarity measures for intuitionistic fuzzy sets have been proposed in previous studies, some of those cannot satisfy the axioms of similarity or provide counterintuitive cases. In this paper, a new similarity measure and weighted similarity measure between IFSs are proposed. It proves that the proposed similarity measures satisfy the properties of the axiomatic definition for similarity measures. Comparison between the previous similarity measures and the proposed similarity measure indicates that the proposed similarity measure does not provide any counterintuitive cases. Moreover, it is demonstrated that the proposed similarity measure is capable of discriminating difference between patterns.
机译:作为普通模糊集的一代,以隶属度和非隶属度为特征的直觉模糊集(IFS)概念是应对不确定性的一种更灵活的方法。直觉模糊集的相似性度量用于指示直觉模糊集之间的相似度。尽管在以前的研究中已经提出了许多针对直觉模糊集的相似性度量,但是其中一些不能满足相似性公理或提供了违反直觉的情况。本文提出了IFS之间的一种新的相似性度量和加权相似性度量。证明所提出的相似性度量满足公理定义的相似性度量的性质。先前相似性度量与拟议相似性度量之间的比较表明,拟议相似性度量未提供任何违反直觉的情况。此外,证明了所提出的相似性度量能够区分模式之间的差异。

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