基于AR模型的置信规则库结构识别算法

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 K-means 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 un-certain 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 decision-making.