基于直觉模糊集和最优推荐的信任评价模型

提出一种基于直觉模糊集和最优推荐的信任评价模型。用直觉模糊集描述固有信任属性，对固有信任直觉模糊集构成的集合进行模糊聚类，构造信任向量库存储各节点的信任向量，设计推荐信任的计算公式，并应用离散空间的最优搜索理论提高评价效率。实验结果表明，当网络节点数较大时，该模型能计算推荐信任度，且具有较高的评价效率。

Trust Evaluation Model Based on Intuitionistic Fuzzy Sets and Optimal Recommendation

This paper proposes a trust evaluation model based on intuitionistic fuzzy sets and optimal recommendation.Attributes of inherent trust are described in terms of intuitionistic fuzzy sets.Fuzzy cluster analysis is applied on a set of intuitionistic fuzzy sets of inherent trust for generation trust vector database and storage trust vectors.Calculating formula of recommendation trust is given.Optimal search theory in discrete space is applied for improving the efficiency of calculating recommendation trust degree.Experimental results show that when the network node number is large,this model can get the recommend trust calculation,and have high efficiency evaluation.