A nonlinear filtering method based on principle component analysis (PCA) was proposed according to the statistical characteristics of the Doppler ultrasound blood flow signal and wall thump signal.
根据超声多普勒血流信号和血管壁搏动信号的统计特性，提出了一种基于主元分析的非线性滤波方法。
As to the complicated nonlinear relation existing between running status of gear reducer and characteristic parameters, PCA-based RBF neural network reducer running status diagnostics is put forward.
针对减速箱运行状态和特征参数之间存在的复杂非线性关系，提出了基于主成分分析的RBF神经网络减速箱运行状态诊断方法。
The results of PCA and DPC show that the sampling data have a remarkable nonlinear structure.
PCA和DPC分析结果表明，取样数据具有明显的非线性结构。
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