搜索资源列表
CS
- Matlab编写的压缩感知的库函数，包括稀疏分解和重构。-Written in compressed sensing Matlab library functions, including the sparse decomposition and reconstruction.
wvd
- 这是一个轴承故障分解程序，给予小波变换，小波去噪，小波重构与EMD相结合，有时频谱，边际谱，功率谱-This is a to bearing failure decomposition program given wavelet transform, wavelet denoising wavelet reconstruction combined with EMD, sometimes the spectrum, marginal spectrum, power spectrum
EMD-and-wavelet
- EMD分解和小波重构的一些程序-EMD wavelet decomposition and reconstruction of some programs. . . . . . . . . .
EMD
- 信号EMD分解与重构，特征频带内信号重构-EMD signal decomposition and reconstruction, features the band signal reconstruction
xsj
- 关于一个轴承故障分解程序，里面包含小波去噪，重构等，还有EMD模态分解，并且还有各种功率谱仿真图-About a bearing failure decomposition process, which contains the wavelet de-noising, reconstruction, etc., as well as mode decomposition EMD, and there are a variety of power spectrum simulation map
zhensuzheng
- 二维的EMD分解，恢复物体的三维面形，重构物体的三维图像，以及回复的误差图-Two dimensional EMD decomposition, to restore the three-dimensional shape of the object, the reconstruction of three-dimensional images of objects, as well as the recovery of the error map
EMD-LMD_Denoising
- EMD或者LMD的能量密度与其平均周期的乘积K值，当K值发生较大变化的时候，证实该分量为白噪声，可以剔除，将剩下的分量进行重构，达到去噪的目的。-The product of K EMD or LMD energy density and average cycle value when the K value was changed greatly, the component for white noise can be eliminated, the remaining component
HHT
- 基于经验模态分解法（EMD）的Hilbert-Huang变换（HHT）的MATLAB程序。 可将非平稳信号转换为平稳信号，通过将IMF分量累加重构得到平稳信号 。 主程序为HHT.m，需要用到hhspectrum.m函数、instfreq.m函数（在压缩包内）和已安装的EMD工具箱中emd函数。(The empirical mode decomposition (EMD) method based on Hilbert-Huang transform (HHT) of the MATLAB
ceemdan
- CEEMDAN方法，用于解决EMD分解过程中的模态混叠问题，同常用的EEMD方法相比，其有效减少了迭代次数，增加了重构精度，更加适合非线性信号的分析。(CEEMDAN method is used to solve the modal aliasing problem in the EMD decomposition process. Compared with the commonly used EEMD method, it effectively reduces the number of
emd
- 经验模态分解（希尔伯特黄变换）用于非平稳信号的分解和重构(Empirical mode decomposition)
emd
- emd分解，通过emd分解重构信号实现降噪(By the emd decomposition reconstruction signal noise reduction)
emd
- 经验模态分解（Empirical Mode Decomposition，EMD）是由 Huang等人于1998年提出的一种针对非线性、非平稳信号的自适应信号分解算法。自该方法提出以后便得到了学术界的广泛关注与研究，经过十几年的研究与发展，在理论方面EMD算法取得了进一步的完善。许多国内外学者也将该方法应用到了地球物理领域，并做了深度的研究与探索。与传统的基于Fourier变换的信号分析方法相比，EMD不仅突破了Fourier变换的局限性，而且不存在如小波变换一样需要预选小波基函数的问题，具有良好
新建文件夹
- 用于EMD 信号分解及重构，重构部分需要自己需要改动(For EMD signal decomposition and reconstruction, the reconstruction part needs to be changed by itself.)
emd
- Emd分解，通过峭度和相关系数选择IMF，进行信号重构，小波分解，小波包分解。(Emd decomposition, selecting IMF by kurtosis and correlation coefficient, signal reconstruction, wavelet decomposition, wavelet packet decomposition.)
emd
- emd分解及其重构,分解成对个IMF，然后进行重构(emd emperical modal decomposition)
用于信号的EMD、EEMD、VMD分解
- 用于信号的分解、降噪和重构，实现故障诊断(Used for signal decomposition, noise reduction and reconstruction to realize fault diagnosis)
【EMD重构】
- 对经验模态分解后的各分量IMF进行重构代码，函数可直接调用。(After the empirical mode decomposition, IMF reconstructs the code, and the function can be called directly.)
emd-lstm
- 基于经验模态分解成多个模态和一个残余量，再利用长短神经网络预测分别训练每一个模态和残余量，最后重构结果，得到预测结果(Prediction based on empirical mode decomposition and long short neural network)