EMD (Empirical Mode Decomposition, generally referred to as EMD Algorithm) is the core algorithm of the Hilbert-Huang Transform (HHT). The Empirical Mode Decomposition (EMD) algorithm is defined by the algorithm process and is not defined by the definite theoretical formula. Therefore, it is very difficult to conduct accurate theoretical analysis on it. We can only rely on a large number of digital simulation tests to continuously evaluate its performance. Conduct in-depth research. The purpose of the EMD algorithm is to decompose a signal with poor performance into a set of better-performing IMMF ntrinsic mode functions, and the IMF must satisfy the following two properties: (1) The number of extreme points (maximum or minimum values) of the signal and the number of zero-crossings are equal or at most one difference;

Filename	Size	Update
Matlab runcode\Matlab runcode\eemd.m	5276	2009-10-08
Matlab runcode\Matlab runcode\ex02d.m	5152	2009-10-13
Matlab runcode\Matlab runcode\fa.m	7969	2018-03-15
Matlab runcode\Matlab runcode\FSPHSP.m	3794	2009-10-12
Matlab runcode\Matlab runcode\ifndq.m	3502	2009-10-08
Matlab runcode\Matlab runcode\NCU2009V1.txt	7709	2009-10-27
Matlab runcode\Matlab runcode\nnspa.m	8575	2009-10-27
Matlab runcode\Matlab runcode\nnspe.m	8085	2009-10-27
Matlab runcode\Matlab runcode\nspplota.m	7539	2009-10-09
Matlab runcode\Matlab runcode\nspplote.m	7520	2009-10-09
Matlab runcode\Matlab runcode\ratio1.m	664	2009-10-11
Matlab runcode\Matlab runcode\ratioa.m	914	2009-10-11
Matlab runcode\Matlab runcode\significanceIMF.m	2420	2009-10-08
Matlab runcode\Matlab runcode\signiplotIMF.m	2730	2009-10-08
Matlab runcode\Matlab runcode\test.m	1169	2012-10-01
Matlab runcode\Matlab runcode\子系统\blocknormalize.m	4316	2009-10-08
Matlab runcode\Matlab runcode\子系统\confidenceLine.m	5770	2009-10-08
Matlab runcode\Matlab runcode\子系统\dist_value.m	2115	2009-10-08
Matlab runcode\Matlab runcode\子系统\emax.m	853	2009-10-08
Matlab runcode\Matlab runcode\子系统\emin.m	867	2009-10-08
Matlab runcode\Matlab runcode\子系统\endprocess1.m	9997	2009-10-08
Matlab runcode\Matlab runcode\子系统\endprocess1.p	1296	2009-11-25
Matlab runcode\Matlab runcode\子系统\extrema.m	4361	2009-10-08
Matlab runcode\Matlab runcode\子系统\FAacos.m	3356	2009-10-08
Matlab runcode\Matlab runcode\子系统\FAcosfor.m	3013	2009-10-21
Matlab runcode\Matlab runcode\子系统\FAhilbert.m	1528	2009-10-08
Matlab runcode\Matlab runcode\子系统\FAimphilbert.m	1603	2009-10-27
Matlab runcode\Matlab runcode\子系统\FAquadrature.m	7896	2009-10-08
Matlab runcode\Matlab runcode\子系统\FAzc.m	6695	2009-10-08
Matlab runcode\Matlab runcode\子系统\findcriticalpoints.m	3103	2009-10-08
Matlab runcode\Matlab runcode\子系统\findEE.m	6536	2009-10-08
Matlab runcode\Matlab runcode\子系统\findEEfsp.m	11957	2009-10-12
Matlab runcode\Matlab runcode\子系统\fspecial.m	13153	2009-10-11
Matlab runcode\Matlab runcode\子系统\hilbert.m	2207	2009-10-11
Matlab runcode\Matlab runcode\子系统\hilbertnormalize.m	1683	2009-10-08
Matlab runcode\Matlab runcode\子系统\hilbtm.m	18846	2009-10-27
Matlab runcode\Matlab runcode\子系统\linearnormalize.m	3610	2009-10-08
Matlab runcode\Matlab runcode\子系统\local_max.m	2614	2009-10-09
Matlab runcode\Matlab runcode\子系统\LOD-imf.csv	319505	2009-06-08
Matlab runcode\Matlab runcode\子系统\LOD78.csv	29021	2002-09-25
Matlab runcode\Matlab runcode\子系统\medianfilter.m	515	2009-10-08
Matlab runcode\Matlab runcode\子系统\pchipnormalize.m	3651	2009-10-08
Matlab runcode\Matlab runcode\子系统\skiphilbt_m.m	1525	2009-10-08
Matlab runcode\Matlab runcode\子系统\splinenormalize.m	3645	2009-10-08
Matlab runcode\Matlab runcode\子系统\splinenormalizeep.m	4204	2009-10-08
Matlab runcode\Matlab runcode\子系统	0	2018-03-07
Matlab runcode\Matlab runcode	0	2018-03-07
Matlab runcode	0	2018-03-05

• We are an exchange download platform that only provides communication channels. The downloaded content comes from the internet. Except for download issues, please Google on your own.
• The downloaded content is provided for members to upload. If it unintentionally infringes on your copyright, please contact us.
• Please use Winrar for decompression tools
• If download fail, Try it againg or Feedback to us.
• If downloaded content did not match the introduction, Feedback to us，Confirm and will be refund.
• Before downloading, you can inquire through the uploaded person information

Nothing．