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Vague集的相似度量在模糊推理、模式识别、聚类分析、决策分析等领域的广泛运用,要求所建立的vague集相似度量模型具有较高的区分度及度量结果合乎人的直觉.基于此要求,首先对已有Vague值的相似度量模型在区分度上的不足进行了分析.然后,在分析地基础上,提出了vague值的相似度量建模须考虑的因素.最后建立了Vague集的相似度量模型.数值实验表明,新模型具有较好的区分度,能克服已有模型在区分度上的不足.
The similarity measures of Vague sets are widely used in the fields of fuzzy reasoning, pattern recognition, clustering analysis, decision analysis and so on. It is required that the established similarity measure model of vague sets has high discrimination and the measurement results are in conformity with human intuition. , Firstly, the insufficiency of the similarity measure model of the existing Vague value is analyzed.Secondly, on the basis of analysis, the factors that must be considered in the modeling of similarity measure of vague value are proposed.Finally, Metric model.Numerical experiments show that the new model has a good degree of discrimination, which can overcome the deficiencies of the existing model in the degree of discrimination.