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Adaptive Case Based Reasoning For Fault diagnosis

机译:基于自适应情况的故障诊断

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A hybrid system of Case Based Reasoning (CBR) with Fuzzy ARTMAP (FAM) has been proposed to perform fault diagnosis for actuator system in DAMADICS benchmark. The hybrid system of CBR and FAM is for undertaking the stability plasticity dilemma for the incremental learning problem in CBR. At the same time, FAM can overcome the difficulty of indexing and retrieval in CBR as well as adaption of cases. FAM is used to make hypotheses and to guide the search of similar cases in the library, while CBR is used to select the most similar match for a given problem, supporting a particular hypothesis. A CBR system supports problem solving based on past experience with similar decision problems. The main strength lies in the fact that it enables directly reusing concrete examples in history and consequently eases the knowledge acquisition bottleneck.
机译:已经提出了一种基于案例推理(CBR)的混合系统,具有模糊艺术图(FAM),以对DAMADICS基准中的执行器系统进行故障诊断。 CBR和FAM的杂交系统用于在CBR中进行稳定性可塑性困境。与此同时,FAM可以克服CBR中索引和检索的难度以及病例的适应。 FAM用于制作假设并指导在库中搜索类似情况,而CBR用于选择给定问题的最相似的匹配,支持特定的假设。 CBR系统基于具有类似决策问题的过去的经验支持问题解决。主要优势在于它使其能够在历史中直接重用具体示例,因此可以缓解知识获取瓶颈。

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