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Research on Uncertainty of Rough Fuzzy Sets in Different Knowledge Granularity Levels

机译:不同知识粒度水平下粗糙模糊集的不确定性研究

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The rough fuzzy sets (RFS) is a combination granular computing model with rough sets and fuzzy sets. Its uncertainty includes roughess, rough entropy, fuzziness and fuzzy entropy, etc. In this paper, the changes of roughness, cut-set and fuzziness are discussed according to the knowledge granularity in different knowledge granularity levels in apporiximation spaces of rough fuzzy sets. Hence, the regularity of the uncertainty of rough fuzzy sets in different knowledge granularity levels is discovered, namely the roughness and fuzziness of rough fuzzy sets will decrease with the refinement of knowledge granularity in approximation spaces, and the cut-set of lower approximations will increase and the cut-set of upper approximations will decrease.
机译:粗糙模糊集(RFS)是具有粗糙集和模糊集的组合粒度计算模型。其不确定性包括粗糙程度,粗糙熵,模糊性和模糊熵等。本文根据粗糙模糊集的近似空间中不同知识粒度级别的知识粒度,讨论了粗糙度,割集和模糊性的变化。因此,发现了不同知识粒度水平下粗糙模糊集不确定性的规律性,即随着近似空间中知识粒度的细化,粗糙模糊集的粗糙度和模糊性将降低,较低近似值的割集将增加并且较高近似值的割集将减少。

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