摘要
机构选型多级模糊评判的核心计算是实现隶属度转换;但是,现有隶属度转换方法包含冗余性,表现在指标隶属度中对目标分类不起作用的冗余部分也被用于计算目标隶属度.为此,用基于熵的数据挖掘方法,通过挖掘隐藏在各指标隶属度中关于目标分类的知识信息定义指标区分权;用区分权清除指标隶属度中对目标分类不起作用的冗余数值并提取有效值;有效值经指标重要性权重转化为可比值;用可比值计算目标隶属度实现隶属度转换.由此建立机构选型的改进模糊评判模型.
Implementation of transforming membership degrees is the kernel computation in multilevel fuzzy evaluation of mechanism selection; but the existing algorithms contain redundancy and the redundant parts of the index membership degree, which are useless in objective classification, are involved in calculation of objective membership. Therefore, by using the entropy-based datamining method, through mining the knowledge information hiding in each index membership degree about the objective classification, index-distinguishing weight was defined. By using this weight, the redundant values in index membership degree but useless to objective classification were eliminated and the effective values were extracted. The effective ones were transformed into comparable values through importance weights, which were utilized to calculate objective membership degree to realize membership degree transformation. Based on the above, the improved fuzzy evaluation model of mechanism selection was established.
出处
《工程设计学报》
CSCD
北大核心
2009年第2期88-92,102,共6页
Chinese Journal of Engineering Design
基金
国家自然科学基金资助项目(60474019)
河北省自然科学基金资助项目(F2005000482)
关键词
机构选型
模糊评判
区分权
有效值
可比值
mechanism selection
fuzzy evaluation
divisional right
effective value
comparablevalue
