算是简单的Benchmark吧：将这个模拟信号分离

mayaview

很多信号处理方法都宣称可以将信号分离开来，特别是EMD，下面是一个模拟信号，可以将这个信号分离出来吗？

照理来说，这可是EMD处理的信号中最简单的情况之一了。

x
-1.3923107
-1.7827772
-1.9297508
-1.8667294
-1.6688987
-1.4277928
-1.2236366
-1.1034891
-1.0712097
-1.0915056
-1.1060568
-1.0561847
-0.9047408
-0.6503223
-0.3293914
-0.0056334
0.2501833
0.3836379
0.3760684
0.2537111
0.0832439
-0.0456334
-0.0460849
0.1386664
0.5146758
1.0294671
1.5800643
2.0355889
2.2691256
2.1908536
1.7738307
1.0654640
0.1812203
-0.7184153
-1.4624694
-1.9073023
-1.9686555
-1.6402748
-0.9947749
-0.1667678
0.6774438
1.3761859
1.8061056
1.9055476
1.6827273
1.2077015
0.5910981
-0.0444085
-0.5925062
-0.9847090
-1.2002611
-1.2631113
-1.2279651
-1.1600600
-1.1145630
-1.1211224
-1.1773042
-1.2519802
-1.2969620
-1.2630460
-1.1156785
-0.8458594
-0.4734857
-0.0425893
0.3897978
0.7677572
1.0495493
1.2161308
1.2730974
1.2459846
1.1707312
1.0823675
1.0053068
0.9479428
0.9028326
0.8520048
0.7754068
0.6596212
0.5039903
0.3221653
0.1385588
-0.0192031
-0.1294531
-0.1843362
-0.1937208
-0.1836612
-0.1895074
-0.2453825
-0.3729254
-0.5725769
-0.8201453
-1.0700486
-1.2648477
-1.3489426
-1.2830707
-1.0558761
-0.6894213
-0.2369525
0.2268661
0.6211444
0.8759013
0.9471393
0.8263290
0.5421156
0.1539282
-0.2609124
-0.6234977
-0.8692502
-0.9598979
-0.8890234
-0.6803472
-0.3796622
-0.0427803
0.2773649
0.5413552
0.7293623
0.8416781
0.8941930
0.9105480
0.9133875
0.9171578
0.9242662
0.9253599
0.9032973
0.8393995
0.7200372
0.5416428
0.3127911
0.0528894
-0.2120154
-0.4550368
-0.6542074
-0.7966593
-0.8802183
-0.9121872
-0.9058906
-0.8761395
-0.8349982
-0.7890439
-0.7387822
-0.6801909
-0.6077100
-0.5175822
-0.4103810
-0.2918602
-0.1718187
-0.0613290
0.0307788
0.1010107
0.1527988
0.1959304
0.2437993
0.3091390
0.3994077
0.5131877
0.6387676
0.7555396
0.8380978
0.8621740
0.8109931
0.6804312
0.4815801
0.2399099
-0.0089589
-0.2260302
-0.3767637
-0.4381913
-0.4036926
-0.2843448
-0.1065628
0.0933957
0.2767796
0.4098821
0.4701205
0.4494235
0.3543709
0.2033117
0.0213653
-0.1653698
-0.3350010
-0.4732973
-0.5748160
-0.6417369
-0.6810081
-0.7007768
-0.7071628
-0.7022340
-0.6836326
-0.6458046
-0.5823397
-0.4886516
-0.3641832
-0.2135039
-0.0460123
0.1256372
0.2879187
0.4289667
0.5405903
0.6192881
0.6660861
0.6853572
0.6830466
0.6648588
0.6349206
0.5952462
0.5460642
0.4867876
0.4172225
0.3385553
0.2537485
0.1671891
0.0836901
0.0071724
-0.0605176
-0.1202205
-0.1752265
-0.2301676
-0.2892928
-0.3545990
-0.4243807
-0.4926907
-0.5499900
-0.5849497
-0.5870506
-0.5493761
-0.4708983
-0.3576338
-0.2222932
-0.0823962
0.0428025
0.1359369
0.1849121
0.1852980
0.1412003
0.0644228
-0.0278832
-0.1165534
-0.1839959
-0.2172684
-0.2100374
-0.1630906
-0.0834174
0.0178098
0.1278032
0.2346546
0.3292144
0.4060214
0.4632126
0.5016044
0.5233216
0.5304205
0.5239025
0.5033590
0.4672870
0.4139173
0.3422603
0.2530295
0.1491563
0.0357406
-0.0805523
-0.1924863
-0.2933766
-0.3780562
-0.4434621
-0.4887328
-0.5148464
-0.5239500
-0.5185975
-0.5011152
-0.4732499
-0.4361542
-0.3906480
-0.3376142
-0.2783511
-0.2147300
-0.1490830
-0.0838459
-0.0210745
0.0379947
0.0931657
0.1451749
0.1952408
0.2443712
0.2926193
0.3385205
0.3789131
0.4092632
0.4244771
0.4200535
0.3933146
0.3444116
0.2768273
0.1972039
0.1144714
0.0384206
-0.0219991
-0.0603054
-0.0736835
-0.0635020
-0.0350357
0.0035464
0.0427059
0.0732536
0.0879079
0.0823958
0.0559103
0.0108916
-0.0477554
-0.1137820
-0.1807818
-0.2431843
-0.2968764
-0.3393821
-0.3696468
-0.3875647
-0.3934315
-0.3874993
-0.3697562
-0.3399727
-0.2979710
-0.2440095
-0.1791453
-0.1054483
-0.0259909
0.0554007
0.1345762
0.2075707
0.2710737
0.3227551
0.3613881
0.3867662
0.3994640
0.4005238
0.3911617
0.3725630
0.3458035
0.3118847
0.2718359
0.2268197
0.1781787
0.1273916
0.0759426
0.0251438
-0.0240241
-0.0709877
-0.1155303
-0.1576092
-0.1970820
-0.2334195
-0.2654983
-0.2915599
-0.3093810
-0.3166507
-0.3114868
-0.2929797
-0.2616304
-0.2195585
-0.1704028
-0.1189007
-0.0702081
-0.0290833
0.0009055
0.0179853
0.0224727
0.0166934
0.0045449
-0.0092098
-0.0197722
-0.0231025
-0.0165273
0.0009359
0.0285747
0.0642641
0.1049458
0.1471858
0.1876894
0.2236800
0.2530935
0.2745888
0.2874219
0.2912536
0.2859684
0.2715627
0.2481301
0.2159377
0.1755550
0.1279816
0.0747225
0.0177723
-0.0405004
-0.0975522
-0.1509055
-0.1983837
-0.2382912
-0.2695087
-0.2914965
-0.3042175
-0.3080143
-0.3034750
-0.2913227
-0.2723466
-0.2473785
-0.2172996
-0.1830563
-0.1456628
-0.1061754
-0.0656392
-0.0250204
0.0148528
0.0533172
0.0898521
0.1240082
0.1553034
0.1831181
0.2066278
0.2248066
0.2365214
0.2407103
0.2366178
0.2240345
0.2034848
0.1763060
0.1445848
0.1109448
0.0782117
0.0490124
0.0253823
0.0084556
-0.0016985
-0.0060614
-0.0064747
-0.0053090
-0.0050583
-0.0079332
-0.0155268
-0.0286059
-0.0470546
-0.0699609
-0.0958166
-0.1227753
-0.1489165
-0.1724648
-0.1919365
-0.2062036
-0.2144884
-0.2163145
-0.2114449
-0.1998348
-0.1816155
-0.1571096
-0.1268671
-0.0917023
-0.0527095
-0.0112430
0.0311467
0.0728134
0.1121375
0.1476482
0.1781254
0.2026656
0.2207046
0.2320005
0.2365883
0.2347212
0.2268127
0.2133905
0.1950661
0.1725171
0.1464762
0.1177182
0.0870409
0.0552381
0.0230697
-0.0087634
-0.0396309
-0.0689667
-0.0962437
-0.1209387
-0.1424984
-0.1603238
-0.1737855
-0.1822757
-0.1852960
-0.1825644
-0.1741213
-0.1604073
-0.1422880
-0.1210117
-0.0980965
-0.0751598
-0.0537158
-0.0349759
-0.0196882
-0.0080460
0.0003168
0.0062422
0.0108913
0.0155433
0.0213852
0.0293287
0.0398826
0.0530962
0.0685764
0.0855666
0.1030648
0.1199570
0.1351409
0.1476228
0.1565795
0.1613863
0.1616187
0.1570406
0.1475904
0.1333726
0.1146579
0.0918892
0.0656858
0.0368387
0.0062900
-0.0249069
-0.0556435
-0.0848227
-0.1114269
-0.1345776
-0.1535790
-0.1679411
-0.1773825
-0.1818167
-0.1813272
-0.1761388
-0.1665895
-0.1531065
-0.1361876
-0.1163842
-0.0942867
-0.0705083
-0.0456681
-0.0203740
0.0047909
0.0292766
0.0525674
0.0741768
0.0936380
0.1104983
0.1243199
0.1346951
0.1412765
0.1438199
0.1422345
0.1366284
0.1273392
0.1149378
0.1001995
0.0840413
0.0674313
0.0512836
0.0363536
0.0231534
0.0118995
0.0025053
-0.0053831
-0.0123114
-0.0189164
-0.0258163
-0.0335103
-0.0423023
-0.0522585
-0.0632020
-0.0747414
-0.0863238
-0.0973019
-0.1070022
-0.1147852
-0.1200922
-0.1224748
-0.1216121
-0.1173155
-0.1095302
-0.0983325
-0.0839285
-0.0666522
-0.0469601
-0.0254215
-0.0027008
0.0204691
0.0433212
0.0650932
0.0850680
0.1026100
0.1171941
0.1284246
0.1360428
0.1399260
0.1400776
0.1366135
0.1297460
0.1197679
0.1070372
0.0919634
0.0749935
0.0565995
0.0372658
0.0174770
-0.0022914
-0.0215814
-0.0399566
-0.0570044
-0.0723381
-0.0855988
-0.0964623
-0.1046503
-0.1099485
-0.1122280
-0.1114689
-0.1077793
-0.1014059
-0.0927309
-0.0822515
-0.0705442
-0.0582144
-0.0458400
-0.0339146
-0.0228017
-0.0127045
-0.0036583
0.0044534
0.0118632
0.0188687
0.0257745
0.0328371
0.0402203
0.0479662
0.0559852
0.0640646
0.0718921
0.0790886
0.0852461
0.0899632
0.0928769
0.0936867
0.0921711
0.0881978
0.0817292
0.0728241
0.0616366
0.0484133
0.0334872
0.0172682
0.0002294
-0.0171111
-0.0342125
-0.0505359
-0.0655690
-0.0788495
-0.0899849
-0.0986664
-0.1046773
-0.1078954
-0.1082908
-0.1059191
-0.1009131
-0.0934727
-0.0838539
-0.0723585
-0.0593237
-0.0451113
-0.0300983
-0.0146677
0.0007999
0.0159333
0.0303768
0.0437944
0.0558755
0.0663392
0.0749413
0.0814832
0.0858215
0.0878802
0.0876614
0.0852524
0.0808289
0.0746494
0.0670421
0.0583822
0.0490640
0.0394690
0.0299340
0.0207253
0.0120207
0.0039040
-0.0036294
-0.0106544
-0.0172909
-0.0236728
-0.0299178
-0.0361011
-0.0422373
-0.0482713
-0.0540795
-0.0594795
-0.0642469
-0.0681348
-0.0708960
-0.0723022
-0.0721612
-0.0703306
-0.0667268
-0.0613313
-0.0541930
-0.0454290
-0.0352219
-0.0238151
-0.0115053
0.0013676
0.0144330
0.0273047
0.0395965
0.0509389
0.0609943
0.0694705
0.0761311
0.0808025
0.0833777
0.0838163
0.0821424
0.0784393
0.0728443
0.0655409
0.0567513
0.0467288
0.0357499
0.0241061
0.0120972
0.0000237
-0.0118190
-0.0231468
-0.0336919
-0.0432085
-0.0514774
-0.0583124
-0.0635658
-0.0671352
-0.0689695
-0.0690740
-0.0675130
-0.0644094
-0.0599401
-0.0543264
-0.0478205
-0.0406884
-0.0331915
-0.0255682
-0.0180185
-0.0106932
-0.0036893
0.0029479
0.0092168
0.0151463
0.0207793
0.0261563
0.0313005
0.0362058
0.0408311
0.0450983
0.0488968
0.0520913
0.0545328
0.0560708
0.0565656
0.0558998
0.0539877
0.0507823
0.0462809
0.0405273
0.0336127
0.0256744
0.0168917
0.0074815
-0.0023098
-0.0122143
-0.0219531
-0.0312470
-0.0398274
-0.0474468
-0.0538882
-0.0589725
-0.0625645
-0.0645760
-0.0649672
-0.0637461
-0.0609662
-0.0567227
-0.0511477
-0.0444055
-0.0366863
-0.0282004
-0.0191723
-0.0098346
-0.0004227
0.0088315
0.0177037
0.0259832
0.0334766
0.0400133
0.0454493
0.0496720
0.0526043
0.0542076
0.0544849
0.0534806
0.0512802
0.0480056
0.0438090
0.0388643
0.0333566
0.0274705
0.0213799
0.0152376
0.0091693
0.0032693
-0.0023987
-0.0077986
-0.0129146
-0.0177434
-0.0222846
-0.0265324
-0.0304684
-0.0340578
-0.0372473
-0.0399666
-0.0421326
-0.0436550
-0.0444435
-0.0444151
-0.0435014
-0.0416548
-0.0388539
-0.0351070
-0.0304545
-0.0249694
-0.0187565
-0.0119501
-0.0047102
0.0027824
0.0103323
0.0177364
0.0247911
0.0312998
0.0370807
0.0419725
0.0458407
0.0485815
0.0501250
0.0504365
0.0495166
0.0474003
0.0441548
0.0398762
0.0346863
0.0287281
0.0221614
0.0151585
0.0078990
0.0005656
-0.0066605
-0.0136041
-0.0201001
-0.0259973
-0.0311625
-0.0354837
-0.0388734
-0.0412715
-0.0426468
-0.0429988
-0.0423572
-0.0407808
-0.0383544
-0.0351845
-0.0313936
-0.0271136
-0.0224783
-0.0176171
-0.0126486
-0.0076759
-0.0027843
0.0019598
0.0065070
0.0108224
0.0148806
0.0186612
0.0221440
0.0253047
0.0281122
0.0305277
0.0325045
0.0339903
0.0349302
0.0352708
0.0349644
0.0339738
0.0322759
0.0298657
0.0267583
0.0229908
0.0186229
0.0137361
0.0084323
0.0028312
-0.0029334
-0.0087175
-0.0143718
-0.0197466
-0.0246977
-0.0290907
-0.0328059
-0.0357426
-0.0378223
-0.0389905
-0.0392191
-0.0385058
-0.0368742
-0.0343726
-0.0310716
-0.0270621
-0.0224518
-0.0173622
-0.0119251
-0.0062789
-0.0005649
0.0050762
0.0105082
0.0156022
0.0202403
0.0243185
0.0277491
0.0304635
0.0324138
0.0335739
0.0339408
0.0335333
0.0323920
0.0305763
0.0281618
0.0252362
0.0218954
0.0182384
0.0143628
0.0103615
0.0063186
0.0023077
-0.0016087
-0.0053796
-0.0089633
-0.0123263
-0.0154397
-0.0182774
-0.0208133
-0.0230198
-0.0248672
-0.0263230
-0.0273534
-0.0279251
-0.0280069
-0.0275730
-0.0266050
-0.0250951
-0.0230477
-0.0204816
-0.0174304
-0.0139435
-0.0100853
-0.0059339
-0.0015801
0.0028760
0.0073272
0.0116628
0.0157724
0.0195491
0.0228935
0.0257168
0.0279437
0.0295152
0.0303897
0.0305452
0.0299787
0.0287069
0.0267651
0.0242057
0.0210969
0.0175202
0.0135681
0.0093415
0.0049467
0.0004927
-0.0039117
-0.0081605
-0.0121536
-0.0157992
-0.0190158
-0.0217350
-0.0239023
-0.0254793
-0.0264441
-0.0267917
-0.0265341
-0.0256990
-0.0243283
-0.0224763
-0.0202070
-0.0175909
-0.0147023
-0.0116161
-0.0084049
-0.0051371
-0.0018748
0.0013268
0.0044197
0.0073629
0.0101210
0.0126634
0.0149630
0.0169944
0.0187334
0.0201563

Rainyboy

分解算法什么的我不懂……
但是这个问题不能用曲线拟合解决么？

大喜

好多的数啊，学习学习。。。。

mayaview

Rainyboy 发表于 2015-4-17 21:23
分解算法什么的我不懂……
但是这个问题不能用曲线拟合解决么？

当然可以哦，不过就有人想用EMD来做，还发了不少文章。

解决这个方法，曲线拟合确实是不错的方法，就是初值选择有些时候有点敏感，不注意就收敛不了。

我是想看看EMD之类的算法真的可以把这个信号分开吗？反正我是已经失败了。照理来说那么简单地信号，应该可以做吧。

Rainyboy

mayaview 发表于 2015-4-18 10:12
当然可以哦，不过就有人想用EMD来做，还发了不少文章。

解决这个方法，曲线拟合确实是不错的方法，就 ...

哈哈,那就只有坐等信号处理大神指点了.
另外,python 这些库是我吃饭的家伙，当然熟啦

airazor

EMD的分辨率是频率的1/2，你的这个信号，五个频率为：0.14～0.54，EMD一般只能分解出三个。
你说的拟合我不懂，大概需要知道原始信号的规律表达式，才能拟合吧。
EMD可以用来分解实际信号，按照频率的规律进行分解，用在你这个问题上面也许不太适合。

cufflink

6楼解释的好

mayaview

airazor 发表于 2015-6-13 18:55
EMD的分辨率是频率的1/2，你的这个信号，五个频率为：0.14～0.54，EMD一般只能分解出三个。
你说的拟合我 ...

EMD的分辨率是频率的一半是什么意思？为什么有那么个说法？

airazor

mayaview 发表于 2015-6-19 20:32
EMD的分辨率是频率的一半是什么意思？为什么有那么个说法？

这个是分解程序的本身缺点，无法改正。就像人不能像兔子一样看见侧面的东西。

sh_lin30

airazor 发表于 2016-2-12 18:47
这个是分解程序的本身缺点，无法改正。就像人不能像兔子一样看见侧面的东西。

是不是可以说明确 emd 分辨率是什么 频率是哪个频率

Apologize

需要初值？难道还要迭代吗？

怪咖先生

分解程序在哪里