Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 79-84.

• Intelligent Computing • Previous Articles     Next Articles

Structure Identification of Belief-rule-base Based on AR Model

CHEN Ting-ting,WANG Ying-ming   

  1. Department of Economics and Management,Fuzhou University,Fuzhou 350116,China
  • Received:2017-07-31 Online:2018-06-20 Published:2018-08-03

Abstract: 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 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 decision-making.

Key words: Aggregate production planning, Autoregressive model, Belief-rule-based reasoning, Clustering algorithm, Evidential reasoning, Structure identification

CLC Number: 

  • TP18.02
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