以直觉模糊熵改进权重的模糊数据关联算法  

Fuzzy Data Association Algorithm Using Intuitive Fuzzy Entropy to Improve the Weight

作　　者：魏书堤[1] 赵辉煌[1] 

机构地区：[1]衡阳师范学院计算机科学与技术系,湖南衡阳421008

出　　处：《模糊系统与数学》2013年第4期173-178,共6页Fuzzy Systems and Mathematics

基　　金：国家自然科学基金资助项目(61070061)

摘　　要：针对多中心点的观测数据存在不确定性的问题,首先将观测数据和预测数据进行直觉模糊化,然后利用直觉模糊熵改进直觉模糊集的数据权重,再计算直觉模糊集之间的加权距离以获得观测与预测数据的隶属度,最后依次搜索最大隶属度实现观测与预测的关联。通过实例将改进的直觉模糊C-均值聚类(IFCM)算法应用于数据关联计算,计算结果表明,存在模糊观测数据情况下,可以比较好的处理距离的权重信息,并得到更好的处理结果,实例证明算法是可行的。Aiming at the uncertainty problem in the center point of the observation data, based on intuitive fuzzy clustering, it proposes a data association algorithm using the intuitive fuzzy entropy to improve weighting. The improved fuzzy C-means clustering （IFCM） algorithm is applied to the data correlation calculation. First intuitive fuzzy the observed data and the prediction data, second using Intuitive Fuzzy Entropy to improve the data weight of intuitive fuzzy sets, third caculating the weighted distances of intuitive fuzzy to obtain the membership degree between observed and projected data, finally search maximum membership degree one by one to achieve the association of observation and prediction. An example of calculation shows that the algorithm is feasible under the condition of existing fuzzy observation data.

关 键 词：数据关联 直觉模糊集合 模糊C-均值聚类 直觉模糊熵 

分 类 号：C934[经济管理—管理学]