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.
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。
Principle component analysis is based on multi-statistics method, and turning into an important way to proceed data for production monitoring and quality control.
主成分分析属多元统计方法，正逐步成为控制领域中一种重要的数据处理方法，用于生产监测和质量控制。
For this purpose, this paper presents a reuse cost optimization oriented, locality principle and instance set decomposition based component refactoring method.
为此，提出一种面向复用成本优化的、基于局部性原理与实例集分解的构件重构方法。
应用推荐