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。
The concepts and its filter of wavelet transform are introduced, the application of wavelet as a kind of new analysis tools in unstable signal analysis is presented.
介绍了小波变换的基本概念和滤波特性，给出了小波变换作为一种新的分析工具在非平稳信号分析中的应用方法。
Based on the analysis of wavelet packets and traditional adaptive equalization, an adaptive equalizer structure and its adaptive algorithm, based on wavelet packets transform, are presented.
在分析小波包和传统自适应线性均衡的基础上，提出了一种基于小波包变换的自适应均衡器结构，并给出了相应的自适应均衡算法。
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