该方法首先利用小波变换和主成分分析对故障信号进行预处理，然后用处理后的故障特征数据对小波神经网络进行训练和测试。

The method uses wavelet transform and principle component analysis to preprocess fault signal, afterward training and testing wavelet neural network with the preprocessed fault characteristic data.

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本文运用BP神经网络和主成分分析相结合的方法构建了一个商业银行风险预警模型。

The paper sets up a risk early-warning model using the methods of BP neural network and principal component analysis.

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本文采用主成分分析技术对过程数据降维，然后用降维后的数据训练神经网络，建立软测量模型。

Then, USES PCA to reduce the dimensions of process data, trains the neural network with that data, and establishes the soft sensor.

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