wordpress数据库导入,放心网站推广优化咨询,私人定制网,折扣手游平台app排行榜时序分解 | MATLAB实现CEEMDANSE自适应经验模态分解样本熵计算 目录 时序分解 | MATLAB实现CEEMDANSE自适应经验模态分解样本熵计算效果一览基本介绍程序设计参考资料 效果一览 基本介绍 MATLAB实现CEEMDANSE自适应经验模态分解样本熵计算 包括频谱图 附赠案例数据 可直接运行 …时序分解 | MATLAB实现CEEMDANSE自适应经验模态分解样本熵计算 目录 时序分解 | MATLAB实现CEEMDANSE自适应经验模态分解样本熵计算效果一览基本介绍程序设计参考资料 效果一览 基本介绍 MATLAB实现CEEMDANSE自适应经验模态分解样本熵计算 包括频谱图 附赠案例数据 可直接运行 直接替换excel数据即可使用 适合新手小白 程序设计 完整源码和数据获取方式私信回复MATLAB实现CEEMDANSE自适应经验模态分解样本熵计算。 function [modes its]ceemdan(x,Nstd,NR,MaxIter)%---------------------------------------------------------------------- % INPUTs % x: signal to decompose % Nstd: noise standard deviation % NR: number of realizations % MaxIter: maximum number of sifting iterations allowed. % % OUTPUTs % modes: contain the obtained modes in a matrix with the rows being the modes % its: contain the sifting iterations needed for each mode for each realization (one row for each realization) % ------------------------------------------------------------------------- % Syntax % % modesceemdan(x,Nstd,NR,MaxIter) % [modes its]ceemdan(x,Nstd,NR,MaxIter) % %-------------------------------------------------------------------------- % This algorithm was presented at ICASSP 2011, Prague, Czech Republic % Plese, if you use this code in your work, please cite the paper where the % algorithm was first presented. % If you use this code, please cite: % % M.E.TORRES, M.A. COLOMINAS, G. SCHLOTTHAUER, P. FLANDRIN, % A complete Ensemble Empirical Mode decomposition with adaptive noise, % IEEE Int. Conf. on Acoust., Speech and Signal Proc. ICASSP-11, pp. 4144-4147, Prague (CZ) % % ------------------------------------------------------------------------- % Date: June 06,2011 % Authors: Torres ME, Colominas MA, Schlotthauer G, Flandrin P. % For problems with the code, please contact the authors: % To: macolominas(AT)bioingenieria.edu.ar % CC: metorres(AT)santafe-conicet.gov.ar % -------------------------------------------------------------------------xx(:); desvio_xstd(x); xx/desvio_x;modeszeros(size(x)); tempzeros(size(x)); auxzeros(size(x)); acumzeros(size(x)); iterzeros(NR,round(log2(length(x))5));for i1:NRwhite_noise{i}randn(size(x));%creates the noise realizations end;for i1:NRmodes_white_noise{i}emd(white_noise{i});%calculates the modes of white gaussian noise end;for i1:NR %calculates the first modetempxNstd*white_noise{i};[temp, o, it]emd(temp,MAXMODES,1,MAXITERATIONS,MaxIter);temptemp(1,:);auxauxtemp/NR;iter(i,1)it; end;modesaux; %saves the first mode k1; auxzeros(size(x)); acumsum(modes,1);while nnz(diff(sign(diff(x-acum))))2 %calculates the rest of the modesfor i1:NRtamaniosize(modes_white_noise{i});if tamanio(1)k1noisemodes_white_noise{i}(k,:);noisenoise/std(noise);noiseNstd*noise;try[temp, o, it]emd(x-acumstd(x-acum)*noise,MAXMODES,1,MAXITERATIONS,MaxIter);temptemp(1,:);catchit0;tempx-acum;end;else[temp, o, it]emd(x-acum,MAXMODES,1,MAXITERATIONS,MaxIter);temptemp(1,:);end;auxauxtemp/NR;iter(i,k1)it; end;modes[modes;aux];auxzeros(size(x));acumzeros(size(x));acumsum(modes,1);kk1; ———————————————— 版权声明本文为CSDN博主「机器学习之心」的原创文章遵循CC 4.0 BY-SA版权协议转载请附上原文出处链接及本声明。 原文链接https://blog.csdn.net/kjm13182345320/article/details/119920826参考资料 [1] https://blog.csdn.net/kjm13182345320/article/details/129215161 [2] https://blog.csdn.net/kjm13182345320/article/details/128105718