向量型二元语义密度集结算子及其应用

针对多属性决策问题，面向二元语义信息，以向量的形式对决策信息进行集成，提出了向量型二元语义密度加权平均(V-TDWA)算子的信息集结方法.首先，对向量型二元语义密度加权算子及其合成算子的基本构建思路进行了介绍，并对其性质进行了分析.然后，基于信息分布的疏密程度讨论了向量型二元语义信息的分组问题，给出了一种基于向量相似度的聚类方法，在聚类组的基础上，通过最大化熵值法求解不同聚类组的密度权重.最后通过算例对向量型二元语义密度集结算子的应用进行了简要说明.

Vector-Type Two-Tuple Semantic Density Weighted Operator and Its Application

Aiming at the problem of multi-attribute decision making， two-tuple semantic information in the form of vector is integrated and an information aggregation method of vector-type two-tuple semantic density weighted averaging (V-TDWA) operator is proposed. Firstly， the basic idea of the vector-type two-tuple semantic weighted operator and its synthetic operators are introduced， and then their properties are analyzed. Secondly， based on the density of information distribution， the clustering problem of the vector-type two-tuple semantic information is discussed， and a clustering method based on vector similarity is given. Based on the clustering group， the maximum entropy value method is used to solve the density weight of different clustering groups. Finally， an example is given to briefly explain the application of the vector-type two-tuple semantic density aggregation operator.