New algorithm for CBR-RBR fusion with robust thresholds
New algorithm for CBR-RBR fusion with robust thresholds
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DOI：
10.3901/cjme.2012.06.1255
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发表时间：
2012-10
影响因子：
4.2
通讯作者：
Man Xu;H. Yu;Jiang Shen
中科院分区：
文献类型：
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作者：
Man Xu;H. Yu;Jiang Shen
基于案例推理（CBR）和基于规则推理（RBR）的融合系统包含多种融合方法，其任务的特点是推理过程的交错组合。现有的融合方法不能明确来自知识源的数据之间的复杂关系，也不能在一个融合空间中统一表示异构的案例和规则知识。因此，现有的方法无法解决系统脆弱性，由于知识的不确定性和推理的不可靠性。针对这一问题，提出了一种鲁棒阈值的CBR-RBR融合算法（CRFRT）。异构的案例和规则知识统一表示在一个定义的融合酉空间。该方法通过设定鲁棒阈值来区分融合空间中元知识之间的复杂关系，提高系统的知识识别能力。此外，融合推理策略的CRFRT及其过程的基础上，获得的融合推理问题的鲁棒解决方案。最后，通过机器学习中的基准问题对CRFRT进行了验证.与其他CBR和RBR方法相比，推理效率和准确率分别提高了5%和2.2%。系统精度的变化减小了2%~ 3.8%。上述结果表明，CRFRT算法提高了系统的有效性和鲁棒性。提出的CRFRT可以解决复杂智能决策系统的脆弱性问题，为故障诊断提供高质量的性能。
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.