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.
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.
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.
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。
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.
为了比较算法的性能，作者又分别采用了偏最小二乘法、主成分分析结合BP神经网络进行数据处理。
To compare arithmetic performance, the authors also processed the spectral data with partial least squares and PCA-BP neural network.
将这种新的预测模型（主成分神经网络预报模型）与同样根据这些预报因子建立的逐步回归预报模型进行了对比分析。
Predictive capability between the new model (principal components ANN model) and linear regression model for the same predictors is discussed based on the independent samples and historical samples.
在HSV彩色空间将颜色信息和局部空间特征相结合，利用主元神经网络提取主成分；
Color information and local spatial features are combined in the HSV color space in order to obtain principal components by principal component analysis neural networks.
在人工神经网络建模过程中，采用主成分分析的方法对网络的输入数据进行预处理，显著提高了网络的学习速度。
In the process of modeling ANN, in order to accelerate the back-propagation learning process, the inputs of the ANN are processed in advance by principal component analysis.
本文提出一种基于主成分分析法的动态神经网络模型实现高炉铁水含硅量多步预报。
On the basis of this, this paper suggests that analytic hierarchy process(AHP) and principle component analysis(PCA) can .
针对实际水质评价问题，建立了渭河地面水环境质量综合评价的BP神经网络模型，并与单因子法、主成分分析法进行了分析比较。
Aimed at actual question of water quality assessment, the BP neural network model is established which synthetically assesses the surface water quality of the Wei river.
采用主成分分析方法简化神经网络训练样本，进而优化网络的结构。
The principal component analysis method was adopted to simplify the training pattern of neural network, and optimize the structure of the network.
并采用主成分分析法简化神经网络结构，将网络的输入节点数从60维降低到3维。
The PCA reduced the network's input nodes from 60 to 3 to simplify the neural network's structure.
通过主成分分析、聚类分析和BP神经网络对实验数据进行了分析和识别。
Principal component analysis (PCA), cluster analysis (CA) and back-propagation artificial neural network (BP-ANN) were used in the data analysis and pattern recognition.
通过主成分分析、聚类分析和BP神经网络对实验数据进行了分析和识别。
Principal component analysis (PCA), cluster analysis (CA) and back-propagation artificial neural network (BP-ANN) were used in the data analysis and pattern recognition.
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