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导读EMD工具箱,在MATLAB中实现EMD的各种功能所必须的工具箱。运行install_emd.m文件可以实现此工具箱的安装，uninstall_emd.m实现卸载。1、首先...

EMD工具箱,在MATLAB中实现EMD的各种功能所必须的工具箱。

运行install_emd.m文件可以实现此工具箱的安装，uninstall_emd.m实现卸载。

1、首先下载emd工具箱，50楼网址里面可以下。下载后解压放在matlab的work工作路径下package_emd文件夹。

2、打开matlab，选择File- Set Path- Add with Subfolders-你刚才下载的工具箱（package_emd）点进去- Save- Close。

3、此时选择work下package_emd文件夹作为工作路径，即是C:\Program Files\MATLAB\R2010a\toolbox\package_emd。

4、在Command Window里面输入mex -setup回车，问是否选择已有的编译器你选y回车，再问选择哪个编译器，你可以选择C++的那个选择相应的编号（如 2）回车，然后让你核对是否选择对了编译器等等，你输入y回车。就安装成功了

>> mex -setup

Please choose your compiler for building external interface (MEX) files:

Would you like mex to locate installed compilers [y]/n? y

Select a compiler:

[1] Lcc-win32 C 2.4.1 in C:\PROGRA~1\MATLAB\R2010a\sys\lcc

[2] Microsoft Visual C++ 6.0 in C:\Program Files\Microsoft Visual Studio

[0] None

Compiler: 2

Please verify your choices:

Compiler: Microsoft Visual C++ 6.0

Location: C:\Program Files\Microsoft Visual Studio

Are these correct [y]/n? y

Trying to update options file: C:\Users\Administrator\AppData\Roaming\MathWorks\MATLAB\R2010a\mexopts.bat

From template: C:\PROGRA~1\MATLAB\R2010a\bin\win32\mexopts\msvc60opts.bat

Done . . .

**************************************************************************

Warning: The MATLAB C and Fortran API has changed to support MATLAB

variables with more than 2^32-1 elements. In the near future

you will be required to update your code to utilize the new

API. You can find more information about this at:

http://www.mathworks.com/support/solutions/en/data/1-5C27B9/?solution=1-5C27B9

Building with the -largeArrayDims option enables the new API.

**************************************************************************

MATLAB的emd的工具箱可是不知道怎么用也不知道怎么用它来处理txt中的数据

但是安装的时候，如果使用的是VS的编译器（mbuild –setup、mex –setup设置），会报找不到complex.h的问题（用Linux下的Matlab不会出错），从而使cemdc2_fix.c等文件编译失败，这几个文件是为了快速实现计算EMD而用c编写的，所以即使编译失败，也不影响直接使用emd.m实现EMD功能。如果想编译成功，可如下修改：（摘自http://www.chinavib.com/thread-79866-1-1.html）

G. Rilling 07年3月份的程序，运行作者的install_emd.m，出现找不到complex.h的问题，以下是个人的理解和解决过程：（个人的运行环境为matlab6.5）

complex.h的问题

产生原因：采用matlab的C编译函数mex时，定义了C99_OK的宏（EMDS/make_emdc.m (28行)），利用的是ANSI C99标准如果个人的电脑中没有相关的支持，就会出现这个问题。

解决方法：EMDS/make_emdc.m中第28行中mex(’-DC99_OK‘,args(:))语句中的 '-DC99_OK' 即可。

注意：

改完之后，运行install_emd，会出现M_PI没有定义的问题，缺少了常数PI的宏定义，导致一些.c文件编译失败。

产生原因：去掉C99_OK之后，程序中使用的是作者提供的 emd_complex.h和emd_complex.c两个文件来支持复数运算，这两个文件中，并没有定义M_PI这个宏。

解决方法：M_PI这个宏，只在两个文件中（clocal_mean.c和clocal_mean2.c）使用，个人的解决方法是，在相应的头文件（clocal_mean.h和clocal_mean2.h）中加入M_PI的宏定义即可。

在两个.h文件中分别加入一下语句：

#define CLOCAL_MEAN_H

#ifndef M_PI

#define M_PI 3.1415926

#endif

安装完成后，编译输出的.dll文件会出现，重复后缀名的问题，及 xxx.dll 变成了 xxx.dll.dll自己去掉多余的.dll即可

最后，关于版本问题：作者推荐使用7.1+版本，但只是针对个别的函数有影响，主要是作者提供的例子程序，无法在matlab6.5环境中运行，算法的主要功能函数并不受影响。

工具箱函数

运行help index_emd可以查看工具箱提供的函数，如下

index_emd.M list of functions in the EMD package type help function_name for more information ona specific function Empirical Mode Decomposition emd - computes EMD and bivariate/complex EMD with various options emd_local -computes local EMD variation emd_online - computes on-lineEMD variation. Note that it does not truly apply on-line: the function is only a demonstration. emdc - fast implementation for EMD with Cauchy-like stoppingcriterion (requires compilation, see make_emdc function) emdc_fix - fast implementation for EMD with predefined number ofiterations (requires compilation, see make_emdc function) cemdc - fast implementation for bivariate/complex EMD (firstalgorithm) with Cauchy-like stopping criterion (requires compilation, see make_emdc function) cemdc_fix -fast implementation for bivariate/complex EMD (firstalgorithm) with predefined number of iterations (requires compilation, see make_emdc function) cemdc2 - fast implementation for bivariate/complex EMD (secondalgorithm) with Cauchy-like stopping criterion (requires compilation, see make_emdc function) cemdc2_fix - fastimplementation for bivariate/complex EMD (second algorithm) with predefined number of iterations (requires compilation, see make_emdc function) Utilities install_emd - setup Matlab'spath and compile the C codes. uninstall_emd - revert the modifications made by install_emd andremove the files (optional). make_emdc - compile all C codes emd_visu - visualization of EMD cemd_visu - visualization of bivariate/complex EMD (automaticallycalled by emd_visu when the input is complex) cenvelope - compute envelope curves for bivariate/complex EMD cemd_disp - visualization of envelope curves and tube envelope plot3c - plot a complex vector in 3 dimensions plotc - plot the projection of a complex vector on a variabledirection dirstretch -directional stretching of a complex vector hhspectrum -compute Hilbert-Huang spectrum (need the Time-FrequencyToolbox http://tftb.nongnu.org) toimage - transform a spectrum made of 1D functions (e.g., output of hhspectrum) in an 2D image disp_hhs - display the image output of toimage as a Hilbert-Huangspectrum addtag - add a tag to a graphic object (uses the Tag property as alist of keywords or tags) rmtag - remove a tag from a graphic object (uses the Tag propertyas a list of keywords or tags) hastag - test whether a graphic object has a specific tag (uses theTag property as a list of keywords or tags) findtag - find objects having a specific tag (uses the Tag propertyas a list of keywords or tags) Examples from G. Rilling, P. Flandrin and P.Gon鏰lves, OnEmpirical Mode Decomposition and its algorithms IEEE-EURASIPWorkshop on Nonlinear Signal and Image Processing NSIP-03,Grado (I), June 2003 emd_fmsin - Fig. 1: a 3-component example (need the Time-Frequency Toolbox http://tftb.nongnu.org) emd_triang - Fig. 2: another 3-component example emd_sampling - Fig. 3: effect of sampling on 1 tone emd_separation - Fig. 4: separation of 2 tones ex_online - Sect 3.4: the way emd_online.m works triangular_signal - subroutine called by emd_triang (formerlytriang.m) Examples from G. Rilling, P. Flandrin, P.Gon鏰lves and J. M. Lilly, BivariateEmpirical Mode Decomposition, SignalProcessing Letters (submitted) bivariate_EMD_principle - Fig. 1: principle of the bivariate/complex EMD bivariate_EMD_mean_definitions - Fig. 2: definition of the mean foreach algorithm. Also allows to test other signals and parameter sets. bivariate_EMD_illustration - Fig. 3: illustration of the bivariate EMD on an oceanographic float position record

稍做整理如下：

来自《On Empirical Mode Decomposition and itsalgorithms》的Examples

来自《Bivariate Empirical Mode Decomposition》的Examples

EMD

clc

clear all

close all

% 原始数据

fs = 1000;

ts = 1/fs;

t=0:ts:0.3;

z=2*sin(2*pi*10*t) + 5.*sin(2*pi*100*t);