Matlabmangxinhao
所属分类：matlab编程
开发工具：matlab
文件大小：11096KB
下载次数：45
上传日期：2009-11-12 09:56:04
上 传 者：
lvchenglin123
说明： 该文件能够实现盲信号的分离，分离效果很好，输入为两种信号的非线性叠加，经过仿真效果很明显！
(The document can achieve blind signal separation, separation a good effect, input non-linear superposition of two kinds of signals, through simulation effect is obvious!)
文件列表:
ICALABSPv2_2\acrsobibpf.p (10381, 2004-02-24)
ICALABSPv2_2\acsobiro.m (6091, 2004-02-23)
ICALABSPv2_2\acsobiro.p (7287, 2004-02-24)
ICALABSPv2_2\adv_param.p (28812, 2004-02-24)
ICALABSPv2_2\amuse.m (1477, 2004-02-23)
ICALABSPv2_2\ard2ica.p (24786, 2004-02-24)
ICALABSPv2_2\ardkica.p (22765, 2004-02-24)
ICALABSPv2_2\arngica.p (21778, 2004-02-24)
ICALABSPv2_2\bse1_cum.p (14268, 2004-02-24)
ICALABSPv2_2\bselp.p (7180, 2004-02-24)
ICALABSPv2_2\choseleng.p (6296, 2004-02-24)
ICALABSPv2_2\chosemat.p (11951, 2004-02-24)
ICALABSPv2_2\chosevar.p (4364, 2004-02-24)
ICALABSPv2_2\Contents.m (1501, 2004-02-23)
ICALABSPv2_2\display_chn.p (26814, 2004-02-24)
ICALABSPv2_2\erica.m (5864, 2004-02-23)
ICALABSPv2_2\evd.p (3730, 2004-02-24)
ICALABSPv2_2\evd24.p (7680, 2004-02-24)
ICALABSPv2_2\fapfv.p (2812, 2004-02-24)
ICALABSPv2_2\fard2ica.p (25968, 2004-02-24)
ICALABSPv2_2\fasticater.p (17772, 2004-02-24)
ICALABSPv2_2\flexica.m (3180, 2004-02-23)
ICALABSPv2_2\fobi.p (3395, 2004-02-24)
ICALABSPv2_2\fobip.p (3096, 2004-02-24)
ICALABSPv2_2\getalgbyname.p (883, 2004-02-24)
ICALABSPv2_2\getalgotype.p (81818, 2004-02-24)
ICALABSPv2_2\getval.p (2061, 2004-02-24)
ICALABSPv2_2\icacalcpi.m (1130, 2004-02-23)
ICALABSPv2_2\icaedmat.p (2119, 2004-02-24)
ICALABSPv2_2\icahelp.p (802, 2004-02-24)
ICALABSPv2_2\icalab.html (94852, 2004-02-24)
ICALABSPv2_2\icalab.p (120322, 2004-02-24)
ICALABSPv2_2\icalab2.p (98642, 2003-11-21)
ICALABSPv2_2\icalab_alg_is_run.p (4144, 2004-02-24)
ICALABSPv2_2\icalab_clearw.p (11170, 2004-02-24)
ICALABSPv2_2\icalab_closechn.p (10770, 2004-02-24)
ICALABSPv2_2\icalab_config.p (6356, 2004-02-24)
ICALABSPv2_2\icalab_deflation.p (45711, 2004-02-24)
ICALABSPv2_2\icalab_drawhead.p (92221, 2004-02-24)
ICALABSPv2_2\icalab_drawhead_scatter.p (30665, 2004-02-24)
... ...
ICALAB for Signal Processing - BENCHMARKS August 15, 2002
The directory Benchmarks contains typical signals and images
for testing and comparison of various algorithms for
ICA, BSE and BSS.
The most interesting benchmarks are briefly described below.
1.
ACsin10d.mat - contains 10 sine-wave sources of the following form:
s_n = sin ((2n-1)*w*k) for n = 1,2,....,10.
The sources can be easily separated by the second orders
statistics methods (SOS) like AMUSE, EVD or SOBI algorithms.
However, the higher order statistics (HOS)
ICA algorithms fail to reconstruct such sources, because
they are dependent.
It is interesting to note that different ICA algorithms
(for example JADE and Natural gradient algorithms) give usually
different (inconsistent) results ("independent components")
for this benchmark.
Acsin4d.mat benchmark is similar to acsin10d.mat
but it contains only 4 sources.
2.
ACsparse10.mat - contains 10 sparse (smooth bell-shape) sources
that are approximately independent.
The SOS blind source separation algorithms fail
to separate such sources.
Also some ICA algorithms have failed to separate such sources.
Please try ICALAB to compare performance of various algorithms.
3.
ACvsparse10.mat - contains 10 very sparse (short regular pulses).
Second order BSS algorithms fail to separate these sources.
4.
ABio7.mat - this benchmark contains 7 typical biological sources.
This benchmark was proposed by Allan Barros.
5.
Sergio7.mat - this benchmark contains 7 sources (some of them
are asymmetric distributed).
This benchmark was proposed by Sergio Cruces.
6.
AC10-7sparse -contains 10 sensors signals which are mixture
of 7 sources (extracted from the file ACsparse10.mat).
7.
acspeech16.mat contains 16 typical speech signals which have
temporal structure but they are not precisely independent.
Similar benchmarks are :
Speech4.mat, Speech8.mat. Speech10.mat and Speech20.mat with
4, 8, 10 and 20 sounds (speech and music) sources.
10halo.mat contains 10 speech signals highly correlated
(the all 10 speakers say the same sentence).
8.
nband5.mat contains 5 narrow band sources.
This is standard "easy" benchamark.
9.
Gnband.mat contains 5 fourth order colored sources with distribution close
to Gaussian. This is rather "difficult" benchmark. Please try program JADE-TD
to separate the signals from their mixture.
10.
EEG19.mat consists 19 EEG signals with clear heart, eye movements
and eye blinking artifacts.
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