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基于限定样本序贯极端学习机的模拟电路在线故障诊断

DOI: 10.13195/j.kzyjc.2013.1620, PP. 455-460

Keywords: 序贯极端学习机,模拟电路,故障诊断,限定样本,相似度

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Abstract:

为解决故障特征样本分批加入时分类模型的在线更新问题,提出一种限定样本序贯极端学习机(LSSELM).LSSELM通过逐步添加新样本,同时剔除与其相似度最高的同类别旧样本来提高模型的动态适应能力,并通过Sherman-Morrison矩阵求逆引理来降低计算复杂度,实现输出权值的递推求解,完成模型的在线训练.将LSSELM用于模拟电路在线故障诊断,结果表明相比在线序贯极端学习机(OS-ELM)和LSSELM的诊断准确率更高,具有更好的泛化性能.

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