摘要
针对以置信规则推理作为系统控制器的应用,传统的置信K均值聚类算法往往不能充分利用数据中时间上的动态关联信息。因此,在模糊聚类算法的基础上引入自回归(AR)模型,将集约生产计划中的需求数据作为一组时间序列进行动态的聚类分析。该算法不仅可以充分利用集约生产计划中的需求数据的内部自相关性,而且可以进一步利用隶属度函数对AR模型的预测过程进行模糊化调整,从而得到更为理想的置信规则库结构,提高推理与决策的精度。
According to the application of the belief-rule based reasoning in system control,the traditional belief Kmeans clustering algorithm can not make full use of the dynamic correlation information of time in data.Therefore,based on the fuzzy clustering algorithm,the autoregressive（AR）model was introduced to dynamically cluster the uncertain demand in the aggregate production planning as a set of time series.Compared with traditional algorithm,the new algorithm has the following characteristics.It can not only make full use of the aggregate demand data within the correlation of the production plan,but also further use the membership functions of the AR model to predict process fuzzy adjustment,so as to get more ideal belief rule base structure and improve the accuracy of reasoning and decisionmaking.
作者
陈婷婷
王应明
CHEN Ting- ting, WANG Ying -ming(Department of Economics and Management,Fuzh u Unversty,Fuzhu 35011G,Chin)
出处
《计算机科学》
CSCD
北大核心
2018年第B06期79-84,共6页
Computer Science
基金
国家自然科学基金项目(71501047)资助
关键词
置信规则推理
证据推理
结构识别
聚类算法
集约生产计划
AR模型
Belief rule based reasoning
Evidential reasoning
Structure identification
Clustering algorithm
Aggre gateprod uction planning
Autoregressive model
