基于Manhattan距离的正态云模型相似性度量方法研究
Manhattan Distance Based Measurement of Similarity for Normal Cloud Model
詹媛媛 1周金明 1陶胜男 1李龙莲1
作者信息
- 1. 安徽工程大学数理与金融学院,安徽 芜湖 241000
- 折叠
摘要
综合考虑正态云的期望曲线与含熵期望曲线,提出了一种基于Manhattan距离的正态云相似性度量方法.首先,将不确定性语言信息转化为定量数值,分别计算群体云评价与最优云、最劣云之间的相似度,采用加权平均集成给出综合相似度,从而得到各方案的排序.最后,通过实例说明该方法的有效性.
Abstract
This article considers the expectation curve and expectation curve with the entropy of normal clouds,and proposes a similarity measurement method for normal clouds based on Manhattan dis-tance.Firstly,the uncertain linguistic information is transformed into quantitative numerical values.Then,the similarity between the group cloud evaluation and the best and worst clouds is calculated separately.The weighted average ensemble is used to obtain the comprehensive similarity,thereby ob-taining the ranking of each scheme.Finally,the effectiveness this method are demonstrated through ac-tual examples.
关键词
正态云/期望曲线/Manhattan距离/相似度Key words
normal cloud/expectation curve/Manhattan distance/similarity引用本文复制引用
出版年
2024
NETL
NSTL
万方数据