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xinhaochuli

于 2013-10-10 发布 文件大小:229KB
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下载积分: 1 下载次数: 232

代码说明:

  近期成果,与大家分享:奇异值分解、小波降噪、zoom细化 谱峭度 和滤波器设计,共勉(Recent achievements to share with you: singular value decomposition, wavelet noise reduction, zoom refined spectral kurtosis and filter design, encouragement)

文件列表:

Matlab实现数字FIR的高通_、带通、低通、带阻滤波器的程序.doc,56832,2013-06-06
Pack Kurtogram
..............\Pack Kurtogram V3
..............\.................\binary.m,383,2011-07-10
..............\.................\DBFB.m,793,2011-07-10
..............\.................\demo_Fast_Kurtogram.m,479,2011-05-19
..............\.................\Fast_Kurtogram.m,6035,2011-07-10
..............\.................\Find_stft_kurt.m,2671,2011-07-10
..............\.................\Find_wav_kurt.m,2159,2011-07-10
..............\.................\Kf_fft.m,999,2011-07-10
..............\.................\Kf_W.m,2014,2011-07-10
..............\.................\kurt.m,696,2011-07-10
..............\.................\K_wpQ.m,1354,2011-07-10
..............\.................\K_wpQ_filt.m,1480,2011-07-10
..............\.................\K_wpQ_filt_local.m,925,2011-07-10
..............\.................\K_wpQ_local.m,2430,2011-07-10
..............\.................\max_IJ.m,481,2011-07-10
..............\.................\raylinv.m,894,2011-07-10
..............\.................\TBFB.m,535,2011-07-10
..............\.................\VOIE1.MAT,874144,1999-09-10
..............\ReadmeFK.rtf,4065,2007-09-21
qiyizhi1.m,452,2013-08-18
wavelet.m,623,2013-06-05
zoom.m,1791,2013-06-18

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    程序设计步骤: 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步) 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量(Programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction)
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