计算机科学 ›› 2015, Vol. 42 ›› Issue (6): 233-238.doi: 10.11896/j.issn.1002-137X.2015.06.049
陈云风,王红军,杨燕
CHEN Yun-feng, WANG Hong-jun and YANG Yan
摘要: 聚类集成是对若干独立基聚类器的结果进行组合,从而得到一个对原始数据最优的聚类结果。聚类集成能够减小噪声和孤立点对结果的影响,同时增强聚类结果的鲁棒性和稳定性。从3方面阐述了基于聚类集成的高铁故障诊断分析:1)将原始高铁仿真数据通过傅里叶变化把信号从时域转换到频域,再用不同的特征选择算法进行数据预处理分析;2)分别采用Affinity Propagation(AP)、模糊C均值(FCM)、高斯混合模型(EmGauussian)、Kmeans 4种不同的聚类算法对预处理后的数据进行分析比较;3)引入HGPA、MCLA、CSPA 3种不同聚类集成模型,将得到的基聚类结果分别进行集成。首次把聚类集成算法运用于高铁故障分析中,对比实验结果表明,该方法相比于单个的聚类算法能够更准确有效地进行高铁故障诊断。
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