A Belief K-means Clustering Algorithm for Structure Identification of Belief-rule-base
Guo Min · Systems Engineering · 2011
A belief K-means clustering algorithm is proposed to identify the structure of a belief-rule-base for belief-rule based reasoning in system control.After the inference framework of the belief-rule-base is constructed,the algorithm can generate a reasonable structure of the belief-rule base and improve inference accuracy and decision quality through mining historical data about antecedent input variables.Compared with traditional expert-knowledge based methods for determining the structure of belief-rule-base,the new algorithm has the following characteristics.The generated optimal cluster is directly proportional to the distance between two adjacent evaluation grades,which is consistent with human cognition.The optimal cluster ensures that the sampling data are around the evaluation grades with minimum distances,which ensures that input variables optimally approximate the antecedents of belief rules.A case study is conducted to apply the belief-rule based reasoning to aggregate production planning,which demonstrates the rationality and effectiveness of the proposed algorithm.