针对电火花修整超硬砂轮过程中选择合适放电参数困难的问题,引入基于实例的推理(CBR)和基于规则的推理(RBR)相结合的推理技术,确定了电火花修整超硬砂轮实例表示和实例相似度计算及权值分配的方法,阐述了电火花修整放电规准和规则表示,...针对电火花修整超硬砂轮过程中选择合适放电参数困难的问题,引入基于实例的推理(CBR)和基于规则的推理(RBR)相结合的推理技术,确定了电火花修整超硬砂轮实例表示和实例相似度计算及权值分配的方法,阐述了电火花修整放电规准和规则表示,并以Visual Basic 6.0为开发工具,以SQL Server 2005为底层数据库支持软件,开发了电火花修整超硬砂轮专家系统。将该系统应用于青铜结合剂CBN砂轮NBC160M100的放电参数选择中,实验结果表明,基于CBR-RBR的推理技术可行有效。展开更多
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. Exist...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.展开更多
文摘针对电火花修整超硬砂轮过程中选择合适放电参数困难的问题,引入基于实例的推理(CBR)和基于规则的推理(RBR)相结合的推理技术,确定了电火花修整超硬砂轮实例表示和实例相似度计算及权值分配的方法,阐述了电火花修整放电规准和规则表示,并以Visual Basic 6.0为开发工具,以SQL Server 2005为底层数据库支持软件,开发了电火花修整超硬砂轮专家系统。将该系统应用于青铜结合剂CBN砂轮NBC160M100的放电参数选择中,实验结果表明,基于CBR-RBR的推理技术可行有效。
基金supported by National Natural Science Foundation of China(Grant No. 71171143)National Natural Science Foundation of China Youth(Grant No. 71201087)+2 种基金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)
文摘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.
