小波包分解

其次，亦利用模糊准则对最优小波包分解中特征（小波系数）的分类能力进行评价并排序；

Secondly , the classification abilities of the features ( WP coefficients ) in the optimal WPD are also evaluated and ranked by the fuzzy criterion .

这里，我们首先讨论了小波包分解的过程和最好基及代价函数的选择方法。

Here , first we discuss the process of wavelet packet decomposition and the method for choosing the best base and cost function .

基于小波包分解的SAR图像斑点噪声抑制

Speckle Restraint of SAR Image Based on Wavelet Packets Decomposition

算法以信源熵为标准，搜索ECG的最优小波包分解树。

It searches the optimal wavelet packet decomposition tree guided by entropy .

基于小波包分解及AR模型的单通道地震波信号初至点检测

Onset point identification of single-channel seismic signal based on wavelet packet and the AR model

基于小波包分解的JPEG图像统计特性研究

Statistical Analysis of JPEG Images Based on the Wavelet Packet Decomposition

基于HMM和小波包分解的气液两相流流型识别

Identifying Gas-liquid Two-phase Flow Patterns Based on HMM and Wavelet Packet Decomposition

小波包分解算法及Kalman滤波进行原子钟信号消噪的比较

A Comparison between Wavelet Packets Decomposition Algorithm and Kalman Filter on Reducing the Noise of Atomic Clock Signal

基于小波包分解算法的MPEG-4音频压缩编码的改进与实现

The Development of MPEG-4 Audio Coding System Based on Wavelet Packet Decomposing Algorithm

因此，本文采用小波包分解和BP神经网络对基于声音信号的货运列车滚动轴承故障诊断进行研究。

Therefore , wavelet packet decomposition and BP neural network are used to bearing fault diagnosis of the freight train of sound signals .

利用小波包分解技术对信号进行分析，得到有效的特征量作为BP神经网络的输入样本，并对网络进行学习训练，完成对刀具磨损状态的有效识别。

The selected features are then considered as inputs to BP neural network to complete recognition of the status of the cutting tool .

Shannon小波包分解自适应Gabor滤波器设计及其在纹理分割中的应用

Shannon Wavelet Packets Decomposition Adaptive Gabor Filter and Its Application to Texture Segmentation

针对复杂的小波包分解算法，为了满足振动信号实时分析的要求，设计了一种有效的基于DSP的小波包分解算法。

For the complicated WPT algorithm , we design an effective method based on DSP to meet the real-time requirement of vibration signals analysis .

基于小波包分解和HMM模型的纹理分析

Texture analysis based on WPD and HMM

综合AR参数模型系数和小波包分解能量特征，获得战场声目标特征矢量。

Colligating AR parameter model coefficient and Wavelet packet decomposition energy feature , we get feature vector of battlefield sound-target .

该方法利用小波包分解系数收缩的信号去噪法先对正常工况下的数据进行处理，然后运用T2统计、Q统计方法，结合主元得分图和变量贡献图对一模型进行了仿真研究。

The data collected from the normal industry condition are first processed by means of the wavelet packet coefficient shrinkage .

提出了基于小波包分解自适应Gabor函数设计的纹理分割算法。

The method of designing wavelet packets decomposition adaptive Gabor function to segment texture algorithm is given out .

研究了一种纹理图象的小波包分解及其纹理图象特征的提取方法，并利用BP神经网络进行纹理的分类。

For this purpose , a wavelet packet decomposition and texture feature extraction method has been studied , and the BP neural network model has been used in classification stage .

小波包分解Welch平均法在焊接电弧声功率谱估计中的应用

Application of wavelet packet analysis and Welch method in power spectrum evaluation of arc sound

利用FFT频谱分析小波包分解的低频段，用来诊断转轴的各类故障.阐述了缺陷的种类、可能造成的后果与影响；

This makes it possible to diagnose the different faults of machines . Then the possible consequences and affects due to the software faults are stated .

利用小波包分解对轴承的动态信号进行分析、提取特征，采用RBF神经网络进行承故障诊断。

In testing , Wavelet transformation is used to extract features of dynamic vibration information and then a RBF neural network is instructed to test bearing faults .

为了提高水印图像的攻击鲁棒性和水印的不可见性，该文提出基于人类视觉特性（HVS）和小波包分解的数字水印算法，同时引入信噪比自适应水印嵌入机制。

To increase the robustness and perceptual invisibility , the algorithm is combined with the Humen Visual System ( HVS ) and PSNR adap-tiveness .

结果显示最优搜索的ICA算法运行效率高，信号分离纯度好，对振动信号有高效的降噪作用，并利用小波包分解准确地检测出振动信号中的碰摩信息，其效果优于小波分解法。

Simulation results show that the modified ICA algorithm is effective on denoise of vibra signal , and wavelet packet decomposition may provide richer early rub impact fault information than wavelet decomposition .

对结构第1层加速度响应信号做小波包分解，得到各频段能量的特征向量，作为特征参数输入到BP神经网络中实现结构多处损伤位置和程度识别。

By using wavelet packet decomposing , the quantity of energy over frequency band of the acceleration response of the first story is chosen as the characteristic vector to identify damage , and is used as input variable for BP neural network .

对变转速变载荷工况下滚动轴承的振动信号，用小波包分解法提取各频带的能量作为特征参数，再采用连续隐Markov模型（HMM）对滚动轴承的状态进行识别。

The wavelet envelope decomposition method is adopted to extract energy in different frequency range as character parameters , in turn , continuous hidden Markov model ( HMM ) is adopted to identify the status of the rolling bearing .

最后将测试得到的信号进行小波包分解并提取小波包信号成分节点能量作为测试样本，进行支持向量机SVM模型的预测，识别出结构节点的固结系数。

Finally the measured signal is decomposed and extracted by the WP signal component node energy acted as the test sample , which will carry on the support vector machines SVM model to predict and distinguish fixity factor of the structure node .

本文首先根据小波包分解原理和应用经验总结出小波包特征量与汽机故障对照表，将其与模糊综合评判和BP网络有机结合在一起，建立了基于小波包特征提取的模糊BP诊断网络模型。

According to the principle of wavelet packets and its application experience , a correspondent table which embodies the relation between the turbogenerator faults and the features of wavelet packets is established and a new approach based on the table in tandem with fuzzy BP neural network is presented .

小波包分解不仅包含了图像的低频部分而且还保留了高频部分，它能够有效地提取虹膜纹理特征，并按hamming距离对虹膜进行匹配。

Wavelet packet decomposition retains not only the low but also the high frequency part of iris image , it can effectively extract texture features of iris , and matches the iris by means of hamming distance .

文章分别选取小波包分解终节点的能量和熵作为特征矢量，应用Fisher线性分类器设计了分段线性分类器，对扰动分类进行了仿真识别。

Here , choosing the energy and entropy of terminal nodes through wavelet packet decomposition as feature vectors respectively and applying Fisher linear classifier , the piecewise linear classifier is designed and the simulation and analysis of disturbance classification are carried out .

根据不同谱估计方法的对比结果，提出小波包分解结合Welch平均法对焊接电弧声进行功率谱估计的新方法。

According to the results of comparison between these methods of spectrum evaluation , a new method of power spectrum evaluation of welding arc sound is advanced by means of association of wavelet packet analysis with the Welch average method .