New Algorithm for CBR-RBR Fusion with Robust Thresholds  

作　　者：XU Mad YU Haiyan SHEN Jiang 

机构地区：[1]TEDA College, Nankai University, Tianjin 300457, China [2]College of Management and Economics, Tianjin University, Tianjin 300072, China

出　　处：《Chinese Journal of Mechanical Engineering》2012年第6期1255-1263,共9页中国机械工程学报（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant No. 71171143);National Natural Science Foundation of China Youth(Grant No. 71201087);Tianjin Municipal Research Program of Application Foundation and Advanced Technology of China(Grant No. 10JCYBJC07300);Tianjin Municipal Key Project of Science and Technology Supporting Program of China(Grant No. 09ECKFGX00600);Science and Technology Program of FOXCONN Group(Grant No. 120024001156)

摘　　要：The case-based reasoning（CBR） and rule-based reasoning（RBR） fusion systems include a diverse range of fusion methods and their tasks are characterized by interleaving combination of the reasoning procedures. Existing approaches cannot clarify the complex relationships between data from the knowledge sources nor uniformly represent the heterogeneous case and rule knowledge in one fusion space. As a result, existing approaches fail to solve system fragility due to knowledge uncertainty and reasoning unreliability. For the purpose of addressing the difficulties, a novel algorithm for CBR-RBR fusion with robust thresholds（CRFRT） is proposed. Heterogeneous case and rule knowledge are uniformly represented in one defined fusion unitary space. The robust thresholds have been achieved to distinguish the complex relationships between meta-knowledge in the fusion space and to enhance system capacity of knowledge identification. Furthermore, fusion reasoning strategies are constructed for CRFRT and its procedure based on which robust solution of the fusion reasoning problem is obtained. Finally, CRFRT is validated by benchmark problems in machine learning. Compared with other CBR and RBR approaches, the reasoning efficiency and accuracy are increased by 5% and 2.2% respectively. The variations of system accuracy are decreased by 2% to 3.8%. The above results show that the CRFRT algorithm boosts the system＇s effectiveness and robustness. The proposed CRFRT can solve the fragility of complex intelligence decision system and give quality performance for fault diagnosis.The case-based reasoning（CBR） and rule-based reasoning（RBR） fusion systems include a diverse range of fusion methods and their tasks are characterized by interleaving combination of the reasoning procedures. Existing approaches cannot clarify the complex relationships between data from the knowledge sources nor uniformly represent the heterogeneous case and rule knowledge in one fusion space. As a result, existing approaches fail to solve system fragility due to knowledge uncertainty and reasoning unreliability. For the purpose of addressing the difficulties, a novel algorithm for CBR-RBR fusion with robust thresholds（CRFRT） is proposed. Heterogeneous case and rule knowledge are uniformly represented in one defined fusion unitary space. The robust thresholds have been achieved to distinguish the complex relationships between meta-knowledge in the fusion space and to enhance system capacity of knowledge identification. Furthermore, fusion reasoning strategies are constructed for CRFRT and its procedure based on which robust solution of the fusion reasoning problem is obtained. Finally, CRFRT is validated by benchmark problems in machine learning. Compared with other CBR and RBR approaches, the reasoning efficiency and accuracy are increased by 5% and 2.2% respectively. The variations of system accuracy are decreased by 2% to 3.8%. The above results show that the CRFRT algorithm boosts the system＇s effectiveness and robustness. The proposed CRFRT can solve the fragility of complex intelligence decision system and give quality performance for fault diagnosis.

关 键 词：case-based reasoning （CBR） rule-based reasoning （RBR） robust thresholds fusion reasoning system （FRS） 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置] TP301.6[自动化与计算机技术—控制科学与工程]