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为解决由犹豫模糊数，直觉模糊数，区间数和实数 4 类基本数据组成的多源异类数据的融合识别问题，在犹豫模糊框架内，提出犹豫模糊集相关系数计算方法进行识别判定。首先,将多源异类数据统一用犹豫模糊数描述，其次，分析现有的犹豫模糊集相关系数的局限性，提出了既满足统计学直觉，又不需要各犹豫模糊数中隶属度个数相同，并具有更强的数学概念的新的犹豫模糊集相关系数，最后应用到多源异类数据的融合识别中，利用最大相关系数准则进行识别判定。仿真算例对比分析及验证了新的犹豫模糊集相关系数的有效性，并具有精度高，区分度好的优点。
In order to solve the fusion recognition problem of multi-source heterogeneous data consisting of hedging fuzzy numbers, intuitionistic fuzzy numbers, interval numbers and real numbers of 4 kinds of basic data, hesitating fuzzy sets are proposed to identify the hedging fuzzy sets’ correlation coefficient. First of all, the heterogeneous heterogeneous data are described by using hesitating fuzzy numbers. Secondly, the limitations of the existing hesitating fuzzy sets are analyzed. The results show that both of them satisfy the statistical intuition and do not need the same number of membership , And has a stronger mathematical concept of the new hesitation fuzzy set correlation coefficient, and finally applied to the identification of multi-source heterogeneous data fusion, using the maximum correlation coefficient criteria for identification. The simulation example compares and validates the validity of the new hesitation fuzzy set correlation coefficient, and has the advantages of high precision and good discrimination.