搜索资源列表
wavelet
- 分解信号，分解一段时间序列数据，分解非趋势项之类的(compose a time-data, trend seasonal)
VMD
- VMD变分模态分解是一种全新的自适应信号处理技术，他的实质是若干个自适应维纳滤波组，具有优秀的抗噪声性，性能更强更稳定。(VMD variational mode decomposition is a new adaptive signal processing technology. Its essence is several adaptive Wiener filter banks, which has excellent noise immunity, stronger and more
VMD修改版
- 包含VMD、EMD、EEMD工具箱，可用于变分模态分解、EMD以及EEMD谐波检测对对比分析(Includes VMD, EMD, and EEMD toolboxes for variational modal decomposition, EMD, and EEMD harmonic detection for comparative analysi)
VMD
- vmd，MATLAB程序，针对EMD分解引起的模态混叠问题，能够得到较纯净的IMF分量(VMD, MATLAB program, for the modal aliasing problems caused by EMD decomposition, can get relatively pure IMF components)
emd Vs vmd
- 经验模态分解效果和改进后VMD分解方法对比,尤其是在传统经验模态分解结果的弊端上(The empirical mode decomposition effect is compared with the improved VMD decomposition method, especially in the drawbacks of the traditional empirical mode decomposition results.)
Codes of the VNCMD
- VNCMD分解源程序，未做更改，VMD的拓展研究参考(Source Code for VNCMD)
VMD确定分解层数的程序
- vmd分解层数程序，可以应用到vmd分解层数的确定(Vmd decomposition layer number program, can be applied to the determination of the number of vmd decomposition layers)
Kmeans故障聚类
- C语言做的聚类学习的旋转机械故障诊断，已经经过验证，可以作为参考。(C language clustering learning of rotating machinery fault diagnosis has been verified, and can be used as a reference.)
优化K值
- 分解信号,能量差值优化K，峭度，样本熵，排列熵(Decomposition of signal, optimization of energy difference value K, kurtosis, sample entropy, permutation entropy)
经验模态分解及其改进
- matlab的EMD,EEMD,CEEMD和VMD四种模态分解方式(There are four mode decompositions including EMD, EEMD, CEEMD, and VMD.)
VMD,python代码
- 变分模态分解 信号处理方法 数据序列平稳化。(The data sequence of the signal processing method of variational mode decomposition is smoothed.)
VMD
- 可以将在Sumlink中搭建的模型所呈现的波形数据进行 VMD分解。前提需要有VMD源文件，此处包含，此代码为作者论文分析代码，使用者需进行修改。(The waveform data presented by the model built in sumlink can be decomposed by VMD. The prerequisite is VMD source file, which is not uploaded here. This code is the author's t
iceemdan.m
- 信号iceemdan模态分解，包含测试信号，简单易实现，需要自己安装emd工具箱((It can be used for signal EMD, EEMD, VMD decomposition. Because the algorithm adds its own annotations, it's easy to understand))