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Partition type fuzzy integral model for subjective evaluation processes

机译:主观评价过程的分区式模糊积分模型

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This paper proposes a new fuzzy integral model which can treat interactions among macro-attributes as an effective modeling method. Since a partition of the set of all attributes defines macro-attributes, this fuzzy integral is named partition type fuzzyintegral. An attainment degree to each subset of a partition (i.e., macro-attribute) is evaluated by a vector, so the partition type fuzzy integral is formulated by utilizing the multilinear fuzzy integral of vector valued functions. This method evaluates the interactions of the attributes each of which belongs to a distinct macro-attributes, which means that the evaluation process can be simply explained by fewer parameters. We verify these features by applying this method to a subjective evaluationproblem.
机译:本文提出了一种新的模糊积分模型,该模型可以将宏属性之间的相互作用作为一种有效的建模方法。由于所有属性集的一个分区定义了宏属性,因此该模糊积分称为分区类型Fuzzyintegral。通过向量评估对分区的每个子集(即宏属性)的达到程度,因此,利用向量值函数的多线性模糊积分来制定分区类型的模糊积分。该方法评估每个属性都属于不同的宏属性的交互作用,这意味着可以用较少的参数来简单地解释评估过程。我们通过将此方法应用于主观评估问题来验证这些功能。

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