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基于模糊关联分类器的民机超限事件诊断方法

DOI: 10.13700/j.bh.1001-5965.2013.0656, PP. 1366-1371

Keywords: 飞行品质监控,模糊关联分类器,超限事件,遗传算法,诊断模型

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Abstract:

现有的民用飞机超限事件智能诊断模型大多属于“黑盒”模型,不利于分析超限事件发生的原因.为此提出了一种基于模糊关联分类器(FAC,FuzzyAssociativeClassifier)的民用飞机超限事件诊断方法.该方法抽取发生超限事件时对应的QAR(QuickAccessRecorder)参数快照取值,采用模糊C均值(FCM,FuzzyC-Means)聚类算法对抽取的QAR参数取值模糊预处理,然后基于Apriori算法生成模糊关联分类规则库,并由遗传算法对其进行裁剪,结合模糊分类推理方法形成FAC.采用B737-800实际样本数据进行了验证.实验结果表明,所提出的FAC能有效诊断超限事件,FAC识别超限事件的错误率与最小二乘支持向量机(LS-SVM,LeastSquaresSupportVectorMachine)模型相当,但其解释性方面优于LS-SVM.

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