输入特征选择和输入空间降维是基于神经网络暂态稳定评估的首要问题。
Feature selection and input dimension reduction are of Paramount im-portance to transient stability assessment based on neural networks.
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.
提出一种采用神经网络进行电力系统短期负荷预测的降维方法。
A reduced dimensions method applying neural network is proposed for short term load forecasting.
该方法避免了现有的神经网络降维方法必须对全部属性进行训练和裁剪的弊端，大大提高了属性选择的效率。
It avoids the deficiency of traditional neural network methods needing to train all attributions, which greatly improves the efficiency of attributions selection.
运用了图像进行光照校正，人脸图像进行降维及不同的光照条件下的人脸图像运用改进型的BP神经网络对进行识别。
Use the image illumination correction, reduce the dimension of face images and different lighting conditions, the use of human face images improved the BP neural network for recognition.
运用了图像进行光照校正，人脸图像进行降维及不同的光照条件下的人脸图像运用改进型的BP神经网络对进行识别。
Use the image illumination correction, reduce the dimension of face images and different lighting conditions, the use of human face images improved the BP neural network for recognition.
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