概率语言术语集距离测度的改进及其应用

Improvement and application of distance measure of probabilistic language term set

为了进一步研究模糊多属性决策中的排序问题,提出了一种改进的概率语言术语集距离测度,即在传统距离测度的基础上添加了一项广义距离测度,使得在计算两个不同长度的概率语言术语集之间的距离时,不再需要对初始概率语言信息进行调整,避免了人为添加概率语言术语元所带来的主观影响.利用实例进行计算表明,用该方法计算可信网络社团扩张最优节点时,其排序效果显著优于基于传统距离测度的TOPSIS方法,且具有简洁性;因此,该方法对解决模糊多属性决策中的排序问题具有良好参考.

In order to further study the ranking problems in fuzzy multi-attribute decision-making,an improved probabilistic language term set distance measure was proposed,that is,a generalized distance measure was added to the traditional distance measure,causing when calculating the distance between two probabilistic language term sets of different lengths,it was no longer necessary to adjust the initial probabilistic language information,avoiding the influence of artificially adding probabilistic language term element.The example calculation shows that when the optimal node of trusted network community expansion is calculated by this method,the effect is significantly better than that of the TOPSIS method based on traditional distance measure,and posesses the characteristics of brevity.Therefore,this method can provide a good reference to solve the ranking problems in fuzzy multi-attribute decision-making.