跪求1stOpt4.0以上版本，或者请高人帮忙拟合包含复数的函数的参数
程序代码我已写好，求帮忙拟合
Title 曲线拟合;
ComplexStr = i;
Parameter yibxilong_infite,yibxilong_s,tao,bet_a;
Variable Omega,y[realPart],y[imagPart];
Function y=yibxilong_infite+((yibxilong_s-yibxilong_infite)/((1+i*Omega*tao)^bet_a));
Data;
2 4.37523 0.62119
2.08 4.33845 0.62521
2.16 4.34997 0.63053
2.24 4.31506 0.65416
2.32 4.31287 0.64956
2.4 4.2921 0.6785
2.48 4.27592 0.67085
2.56 4.27271 0.68946
2.64 4.24622 0.69245
2.72 4.24252 0.69161
2.8 4.2235 0.70592
2.88 4.2131 0.69066
2.96 4.20274 0.7088
3.04 4.17474 0.69278
3.12 4.18037 0.69368
3.2 4.15111 0.68694
3.28 4.15204 0.67542
3.36 4.13015 0.68071
3.44 4.11932 0.65229
3.52 4.11557 0.66384
3.6 4.09273 0.64346
3.68 4.10344 0.64157
3.76 4.08152 0.63953
3.84 4.09644 0.63258
3.92 4.07714 0.64613
4 4.07915 0.62682
4.08 4.08866 0.64295
4.16 4.07995 0.64287
4.24 4.09006 0.65073
4.32 4.07778 0.66372
4.4 4.08659 0.65938
4.48 4.09006 0.69362
4.56 4.08228 0.68802
4.64 4.10068 0.71379
4.72 4.0723 0.72193
4.8 4.09413 0.73359
4.88 4.0731 0.75905
4.96 4.0816 0.74975
5.04 4.08084 0.78647
5.12 4.06507 0.77138
5.2 4.0818 0.79578
5.28 4.0533 0.79477
5.36 4.08002 0.7944
5.44 4.0605 0.81608
5.52 4.06823 0.79508
5.6 4.06843 0.82243
5.68 4.05722 0.79832
5.76 4.0792 0.81293
5.84 4.0543 0.81464
5.92 4.07444 0.80338
6 4.05518 0.8267
6.08 4.06451 0.80252
6.16 4.07049 0.84089
6.24 4.04613 0.8307
6.32 4.06599 0.84915
6.4 4.02474 0.86132
6.48 4.04207 0.85187
6.56 4.01568 0.8869
6.64 4.00774 0.861
6.72 4.01495 0.90078
6.8 3.98407 0.88764
6.88 4.01325 0.90488
6.96 3.97776 0.9195
7.04 4.00142 0.90944
7.12 3.99027 0.94878
7.2 3.99063 0.9286
7.28 4.01148 0.96782
7.36 3.98202 0.96614
7.44 4.02715 0.9764
7.52 3.99927 1.00541
7.6 4.0286 0.9848
7.68 4.02259 1.03537
7.76 4.0183 1.00616
7.84 4.04591 1.04756
7.92 4.00783 1.03853
8 4.04809 1.04458
8.08 4.00583 1.06889
8.16 4.02548 1.04709
8.24 4.00542 1.08586
8.32 3.97856 1.05657
8.4 3.99808 1.0767
8.48 3.94704 1.08118
8.56 3.97794 1.066
8.64 3.93227 1.11335
8.72 3.9337 1.07041
8.8 3.93626 1.13698
8.88 3.88795 1.09824
8.96 3.93594 1.15048
9.04 3.86228 1.15887
9.12 3.91482 1.16699
9.2 3.8589 1.22328
9.28 3.88337 1.18775
9.36 3.88369 1.2631
9.44 3.85848 1.23161
9.52 3.90621 1.2869
9.6 3.85492 1.29122
9.68 3.90851 1.28457
9.76 3.8738 1.3413
9.84 3.89225 1.29721
9.92 3.89533 1.37074
10 3.86391 1.32386
10.08 3.91378 1.35101
10.16 3.84783 1.35357
10.24 3.89741 1.33465
10.32 3.84317 1.3746
10.4 3.84082 1.31404
10.48 3.84381 1.36793
10.56 3.79267 1.32282
10.64 3.83601 1.34366
10.72 3.75153 1.35447
10.8 3.78495 1.32402
10.88 3.73595 1.37583
10.96 3.72531 1.32322
11.04 3.73699 1.39732
11.12 3.66998 1.37475
11.2 3.71147 1.40152
11.28 3.64639 1.43016
11.36 3.6951 1.40445
11.44 3.66645 1.49943
11.52 3.64921 1.45173
11.6 3.67278 1.53109
11.68 3.62043 1.50571
11.76 3.70031 1.54225
11.84 3.63168 1.57811
11.92 3.67956 1.54815
12 3.64643 1.61486
12.08 3.65567 1.54865
12.16 3.68399 1.61469
12.24 3.62965 1.58142
12.32 3.69052 1.59474
12.4 3.61013 1.60768
12.48 3.65312 1.56458
12.56 3.60661 1.61913
12.64 3.58948 1.55235
12.72 3.60285 1.6151
12.8 3.52316 1.58248
12.88 3.55328 1.60553
12.96 3.46259 1.6284
13.04 3.4799 1.59773
13.12 3.42929 1.67722
13.2 3.38338 1.63751
13.28 3.37557 1.70799
13.36 3.29446 1.68937
13.44 3.32208 1.7159
13.52 3.23213 1.76005
13.6 3.23184 1.72701
13.68 3.18662 1.79163
13.76 3.1535 1.73895
13.84 3.16797 1.78983
13.92 3.10565 1.77051
14 3.1318 1.75787
14.08 3.07723 1.77302
14.16 3.11093 1.70777
14.24 3.12093 1.74051
14.32 3.10783 1.68152
14.4 3.15819 1.68976
14.48 3.11168 1.66027
14.56 3.18533 1.61469
14.64 3.16197 1.64149
14.72 3.21006 1.57209
14.8 3.22436 1.6219
14.88 3.21211 1.55072
14.96 3.25935 1.57393
15.04 3.20934 1.55437
15.12 3.26857 1.52734
15.2 3.2262 1.55872
15.28 3.23214 1.51635
15.36 3.23624 1.56765
15.44 3.18766 1.52781
15.52 3.21679 1.5537
15.6 3.15515 1.5535
15.68 3.18251 1.53893
15.76 3.13452 1.58305
15.84 3.12766 1.55072
15.92 3.11925 1.6005
16 3.07438 1.58123
16.08 3.09502 1.59905
16.16 3.04198 1.61886
16.24 3.0635 1.62175
16.32 3.03214 1.68653
16.4 3.01135 1.66188
16.48 3.01369 1.71296
16.56 2.97412 1.71076
16.64 3.0108 1.75704
16.72 2.9509 1.79014
16.8 2.94444 1.79493
16.88 2.91727 1.84866
16.96 2.92702 1.82523
17.04 2.93201 1.88693
17.12 2.87968 1.89585
17.2 2.90914 1.91016
17.28 2.86698 1.89892
17.36 2.89035 1.86992
17.44 2.87119 1.90592
17.52 2.86254 1.87056
17.6 2.85872 1.88602
17.68 2.82335 1.8428
17.76 2.86058 1.8239
17.84 2.81505 1.82252
17.92 2.82796 1.7813
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京公网安备 11010802022153号
参考下：
Root of Mean Square Error (RMSE): 0.10112713083366
Sum of Squared Residual: 4.09067863625931
Correlation Coef. (R): 0.976111726914322
R-Square: 0.952794103419661
Adjusted R-Square: 0.952097809581063
Determination Coef. (DC): 0.942348917222362
F-Statistic: 1726.83608880207
Parameter Best Estimate
-------------------- -------------
yibxilong_infite 2.03236571080145
yibxilong_s 4.29651699920008
tao -0.0130589159973196
bet_a 5.00743922766885
能把拟合的图像也发一下吗?
bet_a的范围是0到1之间，能在限制bet_a的范围的条件下拟合出来四个参数的最优值吗？
求助伊始就应该把所有条件说清楚。具体计算数值结果都给出了，自己作下图即可。
Root of Mean Square Error (RMSE): 0.105414296366625
Sum of Squared Residual: 4.44486955138823
Correlation Coef. (R): 0.9713551460971
R-Square: 0.943530819849318
Adjusted R-Square: 0.942684994284388
Determination Coef. (DC): 0.937933024417919
F-Statistic: 1155.43019740749
Parameter Best Estimate
-------------------- -------------
yibxilong_infite 0.57838605541001
yibxilong_s 4.298894670228
tao -0.0423933197104053
bet_a 0.999999999999997
====== Output Results ======
File: Data File-1
No Target y.realpart Calculated y.realpart Target y.imagpart Calculated y.imagpart
1 4.37523 4.27233967168566 0.62119 0.313197913300362
2 4.33845 4.27018950218566 0.62521 0.325536231960443
3 4.34997 4.26795766301072 0.63053 0.337852487711773
4 4.31506 4.26564445483317 0.65416 0.350145878166836
5 4.31287 4.26325018870882 0.64956 0.362415606038233
6 4.2921 4.26077518597362 0.6785 0.37466087930425
7 4.27592 4.2582197781372 0.67085 0.386880911371987
8 4.27271 4.25558430677342 0.68946 0.399074921237995
9 4.24622 4.25286912340785 0.69245 0.411242133646352
10 4.24252 4.25007458940241 0.69161 0.423381779244107
11 4.2235 4.24720107583715 0.70592 0.435493094734052
12 4.2131 4.24424896338919 0.69066 0.447575323024735
13 4.20274 4.2412186422091 0.7088 0.459627713377678
14 4.17474 4.23811051179446 0.69278 0.471649521551744
15 4.18037 4.23492498086107 0.69368 0.483640009944588
16 4.15111 4.23166246721153 0.68694 0.495598447731149
17 4.15204 4.2283233976015 0.67542 0.507524110999143
18 4.13015 4.22490820760357 0.68071 0.519416282881495
19 4.11932 4.22141734146901 0.65229 0.531274253685683
20 4.11557 4.21785125198721 0.66384 0.543097321019932
21 4.09273 4.2142104003431 0.64346 0.554884789916244
22 4.10344 4.21049525597265 0.64157 0.566635972950207
23 4.08152 4.20670629641625 0.63953 0.578350190357558
24 4.09644 4.2028440071705 0.63258 0.590026770147466
25 4.07714 4.198908881538 0.64613 0.601665048212506
26 4.07915 4.1949014204757 0.62682 0.61326436843529
27 4.08866 4.19082213244144 0.64295 0.62482408279174
28 4.07995 4.18667153323917 0.64287 0.636343551450979
29 4.09006 4.18245014586265 0.65073 0.647822142871802
30 4.07778 4.17815850033782 0.66372 0.659259233895737
31 4.08659 4.17379713356397 0.65938 0.670654209836659
32 4.09006 4.16936658915367 0.69362 0.682006464566947
33 4.08228 4.16486741727168 0.68802 0.693315400600178
34 4.10068 4.16030017447282 0.71379 0.704580429170349
35 4.0723 4.15566542353897 0.72193 0.71580097030761
36 4.09413 4.15096373331518 0.73359 0.726976452910516
37 4.0731 4.14619567854505 0.75905 0.738106314814789
38 4.0816 4.14136183970549 0.74975 0.749190002858586
39 4.08084 4.13646280284084 0.78647 0.760226972944289
40 4.06507 4.13149915939652 0.77138 0.771216690096795
41 4.0818 4.12647150605225 0.79578 0.782158628518349
42 4.0533 4.1213804445549 0.79477 0.79305227163989
43 4.08002 4.11622658155119 0.7944 0.803897112168944
44 4.0605 4.11101052842005 0.81608 0.814692652134074
45 4.06823 4.10573290110501 0.79508 0.825438402925888
46 4.06843 4.10039431994651 0.82243 0.836133885334638
47 4.05722 4.09499540951427 0.79832 0.846778629584414
48 4.0792 4.08953679843979 0.81293 0.857372175363963
49 4.0543 4.08401911924905 0.81464 0.867914071854144
50 4.07444 4.07844300819554 0.80338 0.878403877752056
51 4.05518 4.07280910509352 0.8267 0.888841161291854
52 4.06451 4.06711805315185 0.80252 0.899225500262277
53 4.07049 4.06137049880818 0.84089 0.909556482020933
54 4.04613 4.05556709156381 0.8307 0.919833703505346
55 4.06599 4.04970848381911 0.84915 0.930056771240827
56 4.02474 4.04379533070968 0.86132 0.940225301345168
57 4.04207 4.03782828994322 0.85187 0.950338919530228
58 4.01568 4.0318080216373 0.8869 0.960397261100414
59 4.00774 4.02573518815793 0.861 0.970399970948126
60 4.01495 4.01961045395921 0.90078 0.980346703546178
61 3.98407 4.01343448542383 0.88764 0.990237122937248
62 4.01325 4.00720795070476 0.90488 1.0000709027204
63 3.97776 4.00093151956804 0.9195 1.00984772603472
64 4.00142 3.99460586323672 0.90944 1.01956728554009
65 3.99027 3.98823165423607 0.94878 1.02922928339516
66 3.99063 3.98180956624007 0.9286 1.03883343123261
67 4.01148 3.97534027391923 0.96782 1.04837945013162
68 3.98202 3.96882445278981 0.96614 1.05786707058777
69 4.02715 3.96226277906448 0.9764 1.06729603248023
70 3.99927 3.95565592950446 1.00541 1.07666608503646
71 4.0286 3.9490045812732 0.9848 1.08597698679437
72 4.02259 3.94230941179161 1.03537 1.09522850556199
73 4.0183 3.93557109859492 1.00616 1.10442041837477
74 4.04591 3.92879031919121 1.04756 1.11355251145047
75 4.00783 3.92196775092158 1.03853 1.12262458014179
76 4.04809 3.91510407082213 1.04458 1.13163642888668
77 4.00583 3.90819995548764 1.06889 1.14058787115651
78 4.02548 3.90125608093706 1.04709 1.14947872940205
79 4.00542 3.89427312248086 1.08586 1.15830883499736
80 3.97856 3.88725175459023 1.05657 1.16707802818165
81 3.99808 3.88019265076815 1.0767 1.17578615799911
82 3.94704 3.8730964834224 1.08118 1.18443308223689
83 3.97794 3.86596392374052 1.066 1.1930186673611
84 3.93227 3.85879564156672 1.11335 1.20154278845105
85 3.9337 3.85159230528079 1.07041 1.21000532913173
86 3.93626 3.84435458167903 1.13698 1.21840618150454
87 3.88795 3.8370831358572 1.09824 1.22674524607637
88 3.93594 3.82977863109556 1.15048 1.23502243168707
89 3.86228 3.82244172874588 1.15887 1.24323765543546
90 3.91482 3.81507308812069 1.16699 1.25139084260367
91 3.8589 3.80767336638447 1.22328 1.25948192658023
92 3.88337 3.80024321844706 1.18775 1.26751084878169
93 3.88369 3.79278329685912 1.2631 1.27547755857285
94 3.85848 3.7852942517098 1.23161 1.28338201318587
95 3.90621 3.77777673052646 1.2869 1.29122417763803
96 3.85492 3.7702313781766 1.29122 1.29900402464837
97 3.90851 3.76265883677191 1.28457 1.30672153455323
98 3.8738 3.75505974557453 1.3413 1.3143766952207
99 3.89225 3.74743474090537 1.29721 1.32196950196411
100 3.89533 3.73978445605472 1.37074 1.32949995745451
101 3.86391 3.7321095211949 1.32386 1.33696807163226
102 3.91378 3.72441056329517 1.35101 1.34437386161782
103 3.84783 3.71668820603874 1.35357 1.35171735162167
104 3.89741 3.70894306974198 1.33465 1.35899857285352
105 3.84317 3.70117577127573 1.3746 1.36621756343079
106 3.84082 3.69338692398883 1.31404 1.37337436828646
107 3.84381 3.68557713763376 1.36793 1.38046903907633
108 3.79267 3.67774701829437 1.32282 1.38750163408566
109 3.83601 3.66989716831584 1.34366 1.3944722181354
110 3.75153 3.66202818623668 1.35447 1.40138086248782
111 3.78495 3.65414066672283 1.32402 1.40822764475188
112 3.73595 3.64623520050396 1.37583 1.41501264878812
113 3.72531 3.63831237431174 1.32322 1.42173596461323
114 3.73699 3.63037277082024 1.39732 1.42839768830438
115 3.66998 3.62241696858841 1.37475 1.4349979219033
116 3.71147 3.61444554200455 1.40152 1.44153677332012
117 3.64639 3.60645906123285 1.43016 1.44801435623709
118 3.6951 3.59845809216197 1.40445 1.45443079001218
119 3.66645 3.59044319635555 1.49943 1.46078619958258
120 3.64921 3.58241493100476 1.45173 1.46708071536818
121 3.67278 3.57437384888277 1.53109 1.47331447317508
122 3.62043 3.56632049830125 1.50571 1.47948761409904
123 3.70031 3.55825542306865 1.54225 1.48560028442913
124 3.63168 3.55017916245055 1.57811 1.49165263555138
125 3.67956 3.54209225113179 1.54815 1.49764482385267
126 3.64643 3.53399521918047 1.61486 1.50357701062469
127 3.65567 3.52588859201387 1.54865 1.50944936196825
128 3.68399 3.51777289036611 1.61469 1.51526204869769
129 3.62965 3.50964863025763 1.58142 1.52101524624569
130 3.69052 3.50151632296652 1.59474 1.52670913456828
131 3.61013 3.49337647500145 1.60768 1.5323438980503
132 3.65312 3.48522958807652 1.56458 1.53791972541112
133 3.60661 3.47707615908765 1.61913 1.54343680961082
134 3.58948 3.46891668009078 1.55235 1.54889534775679
135 3.60285 3.46075163828164 1.6151 1.55429554101074
136 3.52316 3.45258151597717 1.58248 1.55963759449624
137 3.55328 3.44440679059862 1.60553 1.5649217172067
138 3.46259 3.43622793465615 1.6284 1.57014812191392
139 3.4799 3.42804541573499 1.59773 1.57531702507719
140 3.42929 3.41985969648321 1.67722 1.58042864675293
141 3.38338 3.41167123460091 1.63751 1.58548321050494
142 3.37557 3.40348048283096 1.70799 1.59048094331527
143 3.29446 3.39528788895111 1.68937 1.59542207549571
144 3.32208 3.38709389576762 1.7159 1.60030684059996
145 3.23213 3.37889894111024 1.76005 1.60513547533642
146 3.23184 3.37070345782857 1.72701 1.60990821948175
147 3.18662 3.3625078737898 1.79163 1.61462531579508
148 3.1535 3.35431261187771 1.73895 1.61928700993292
149 3.16797 3.34611808999301 1.78983 1.62389355036493
150 3.10565 3.33792472105498 1.77051 1.6284451882903
151 3.1318 3.32973291300429 1.75787 1.63294217755498
152 3.07723 3.32154306880705 1.77302 1.63738477456971
153 3.11093 3.31335558646008 1.70777 1.6417732382288
154 3.12093 3.30517085899733 1.74051 1.64610782982971
155 3.10783 3.29698927449742 1.68152 1.65038881299353
156 3.15819 3.28881121609233 1.68976 1.65461645358622
157 3.11168 3.28063706197711 1.66027 1.65879101964068
158 3.18533 3.27246718542073 1.61469 1.66291278127977
159 3.16197 3.2643019547779 1.64149 1.66698201064007
160 3.21006 3.25614173350193 1.57209 1.6709989817966
161 3.22436 3.24798688015855 1.6219 1.67496397068835
162 3.21211 3.23983774844071 1.55072 1.67887725504477
163 3.25935 3.23169468718431 1.57393 1.68273911431309
164 3.20934 3.22355804038479 1.55437 1.68654982958656
165 3.26857 3.21542814721467 1.52734 1.69030968353358
166 3.2262 3.20730534204192 1.55872 1.69401896032776
167 3.23214 3.19918995444913 1.51635 1.69767794557889
168 3.23624 3.19108230925351 1.56765 1.70128692626481
169 3.18766 3.18298272652772 1.52781 1.7048461906642
170 3.21679 3.17489152162137 1.5537 1.70835602829033
171 3.15515 3.16680900518334 1.5535 1.71181672982566
172 3.18251 3.15873548318476 1.53893 1.71522858705747
173 3.13452 3.1506712569427 1.58305 1.71859189281433
174 3.12766 3.14261662314453 1.55072 1.72190694090355
175 3.11925 3.13457187387292 1.6005 1.72517402604953
176 3.07438 3.12653729663146 1.58123 1.72839344383307
177 3.09502 3.11851317437089 1.59905 1.7315654906316
178 3.04198 3.11049978551589 1.61886 1.73469046356032
179 3.0635 3.10249740399246 1.62175 1.73776866041431
180 3.03214 3.09450629925579 1.68653 1.74080037961152
181 3.01135 3.0865267363187 1.66188 1.74378592013674
182 3.01369 3.07855897578054 1.71296 1.74672558148643
183 2.97412 3.07060327385655 1.71076 1.74961966361455
184 3.0108 3.06265988240774 1.75704 1.75246846687921
185 2.9509 3.05472904897112 1.79014 1.75527229199036
186 2.94444 3.04681101679041 1.79493 1.75803143995826
187 2.91727 3.03890602484715 1.84866 1.76074621204296
188 2.92702 3.03101430789209 1.82523 1.76341690970464
189 2.93201 3.02313609647712 1.88693 1.76604383455485
190 2.87968 3.01527161698732 1.89585 1.76862728830865
191 2.90914 3.00742109167356 1.91016 1.77116757273766
192 2.86698 2.99958473868522 1.89892 1.77366498962397
193 2.89035 2.99176277210331 1.86992 1.77611984071493
194 2.87119 2.98395540197383 1.90592 1.77853242767887
195 2.86254 2.97616283434137 1.87056 1.78090305206157
196 2.85872 2.96838527128299 1.88602 1.78323201524373
197 2.82335 2.96062291094227 1.8428 1.78551961839921
198 2.86058 2.95287594756363 1.8239 1.78776616245416
199 2.81505 2.94514457152679 1.82252 1.78997194804696
200 2.82796 2.93742896938145 1.7813 1.79213727548902
，
求助伊始就应该把所有条件说清楚。具体计算数值结果都给出了，自己作下图即可。
Root of Mean Square Error (RMSE): 0.105414296366625
Sum of Squared Residual: 4.44486955138823
Correlation Coef. (R): 0.9713551460971
R-Square: 0.943530819849318
Adjusted R-Square: 0.942684994284388
Determination Coef. (DC): 0.937933024417919
F-Statistic: 1155.43019740749
Parameter Best Estimate
-------------------- -------------
yibxilong_infite 0.57838605541001
yibxilong_s 4.298894670228
tao -0.0423933197104053
bet_a 0.999999999999997
====== Output Results ======
File: Data File-1
No Target y.realpart Calculated y.realpart Target y.imagpart Calculated y.imagpart
1 4.37523 4.27233967168566 0.62119 0.313197913300362
2 4.33845 4.27018950218566 0.62521 0.325536231960443
3 4.34997 4.26795766301072 0.63053 0.337852487711773
4 4.31506 4.26564445483317 0.65416 0.350145878166836
5 4.31287 4.26325018870882 0.64956 0.362415606038233
6 4.2921 4.26077518597362 0.6785 0.37466087930425
7 4.27592 4.2582197781372 0.67085 0.386880911371987
8 4.27271 4.25558430677342 0.68946 0.399074921237995
9 4.24622 4.25286912340785 0.69245 0.411242133646352
10 4.24252 4.25007458940241 0.69161 0.423381779244107
11 4.2235 4.24720107583715 0.70592 0.435493094734052
12 4.2131 4.24424896338919 0.69066 0.447575323024735
13 4.20274 4.2412186422091 0.7088 0.459627713377678
14 4.17474 4.23811051179446 0.69278 0.471649521551744
15 4.18037 4.23492498086107 0.69368 0.483640009944588
16 4.15111 4.23166246721153 0.68694 0.495598447731149
17 4.15204 4.2283233976015 0.67542 0.507524110999143
18 4.13015 4.22490820760357 0.68071 0.519416282881495
19 4.11932 4.22141734146901 0.65229 0.531274253685683
20 4.11557 4.21785125198721 0.66384 0.543097321019932
21 4.09273 4.2142104003431 0.64346 0.554884789916244
22 4.10344 4.21049525597265 0.64157 0.566635972950207
23 4.08152 4.20670629641625 0.63953 0.578350190357558
24 4.09644 4.2028440071705 0.63258 0.590026770147466
25 4.07714 4.198908881538 0.64613 0.601665048212506
26 4.07915 4.1949014204757 0.62682 0.61326436843529
27 4.08866 4.19082213244144 0.64295 0.62482408279174
28 4.07995 4.18667153323917 0.64287 0.636343551450979
29 4.09006 4.18245014586265 0.65073 0.647822142871802
30 4.07778 4.17815850033782 0.66372 0.659259233895737
31 4.08659 4.17379713356397 0.65938 0.670654209836659
32 4.09006 4.16936658915367 0.69362 0.682006464566947
33 4.08228 4.16486741727168 0.68802 0.693315400600178
34 4.10068 4.16030017447282 0.71379 0.704580429170349
35 4.0723 4.15566542353897 0.72193 0.71580097030761
36 4.09413 4.15096373331518 0.73359 0.726976452910516
37 4.0731 4.14619567854505 0.75905 0.738106314814789
38 4.0816 4.14136183970549 0.74975 0.749190002858586
39 4.08084 4.13646280284084 0.78647 0.760226972944289
40 4.06507 4.13149915939652 0.77138 0.771216690096795
41 4.0818 4.12647150605225 0.79578 0.782158628518349
42 4.0533 4.1213804445549 0.79477 0.79305227163989
43 4.08002 4.11622658155119 0.7944 0.803897112168944
44 4.0605 4.11101052842005 0.81608 0.814692652134074
45 4.06823 4.10573290110501 0.79508 0.825438402925888
46 4.06843 4.10039431994651 0.82243 0.836133885334638
47 4.05722 4.09499540951427 0.79832 0.846778629584414
48 4.0792 4.08953679843979 0.81293 0.857372175363963
49 4.0543 4.08401911924905 0.81464 0.867914071854144
50 4.07444 4.07844300819554 0.80338 0.878403877752056
51 4.05518 4.07280910509352 0.8267 0.888841161291854
52 4.06451 4.06711805315185 0.80252 0.899225500262277
53 4.07049 4.06137049880818 0.84089 0.909556482020933
54 4.04613 4.05556709156381 0.8307 0.919833703505346
55 4.06599 4.04970848381911 0.84915 0.930056771240827
56 4.02474 4.04379533070968 0.86132 0.940225301345168
57 4.04207 4.03782828994322 0.85187 0.950338919530228
58 4.01568 4.0318080216373 0.8869 0.960397261100414
59 4.00774 4.02573518815793 0.861 0.970399970948126
60 4.01495 4.01961045395921 0.90078 0.980346703546178
61 3.98407 4.01343448542383 0.88764 0.990237122937248
62 4.01325 4.00720795070476 0.90488 1.0000709027204
63 3.97776 4.00093151956804 0.9195 1.00984772603472
64 4.00142 3.99460586323672 0.90944 1.01956728554009
65 3.99027 3.98823165423607 0.94878 1.02922928339516
66 3.99063 3.98180956624007 0.9286 1.03883343123261
67 4.01148 3.97534027391923 0.96782 1.04837945013162
68 3.98202 3.96882445278981 0.96614 1.05786707058777
69 4.02715 3.96226277906448 0.9764 1.06729603248023
70 3.99927 3.95565592950446 1.00541 1.07666608503646
71 4.0286 3.9490045812732 0.9848 1.08597698679437
72 4.02259 3.94230941179161 1.03537 1.09522850556199
73 4.0183 3.93557109859492 1.00616 1.10442041837477
74 4.04591 3.92879031919121 1.04756 1.11355251145047
75 4.00783 3.92196775092158 1.03853 1.12262458014179
76 4.04809 3.91510407082213 1.04458 1.13163642888668
77 4.00583 3.90819995548764 1.06889 1.14058787115651
78 4.02548 3.90125608093706 1.04709 1.14947872940205
79 4.00542 3.89427312248086 1.08586 1.15830883499736
80 3.97856 3.88725175459023 1.05657 1.16707802818165
81 3.99808 3.88019265076815 1.0767 1.17578615799911
82 3.94704 3.8730964834224 1.08118 1.18443308223689
83 3.97794 3.86596392374052 1.066 1.1930186673611
84 3.93227 3.85879564156672 1.11335 1.20154278845105
85 3.9337 3.85159230528079 1.07041 1.21000532913173
86 3.93626 3.84435458167903 1.13698 1.21840618150454
87 3.88795 3.8370831358572 1.09824 1.22674524607637
88 3.93594 3.82977863109556 1.15048 1.23502243168707
89 3.86228 3.82244172874588 1.15887 1.24323765543546
90 3.91482 3.81507308812069 1.16699 1.25139084260367
91 3.8589 3.80767336638447 1.22328 1.25948192658023
92 3.88337 3.80024321844706 1.18775 1.26751084878169
93 3.88369 3.79278329685912 1.2631 1.27547755857285
94 3.85848 3.7852942517098 1.23161 1.28338201318587
95 3.90621 3.77777673052646 1.2869 1.29122417763803
96 3.85492 3.7702313781766 1.29122 1.29900402464837
97 3.90851 3.76265883677191 1.28457 1.30672153455323
98 3.8738 3.75505974557453 1.3413 1.3143766952207
99 3.89225 3.74743474090537 1.29721 1.32196950196411
100 3.89533 3.73978445605472 1.37074 1.32949995745451
101 3.86391 3.7321095211949 1.32386 1.33696807163226
102 3.91378 3.72441056329517 1.35101 1.34437386161782
103 3.84783 3.71668820603874 1.35357 1.35171735162167
104 3.89741 3.70894306974198 1.33465 1.35899857285352
105 3.84317 3.70117577127573 1.3746 1.36621756343079
106 3.84082 3.69338692398883 1.31404 1.37337436828646
107 3.84381 3.68557713763376 1.36793 1.38046903907633
108 3.79267 3.67774701829437 1.32282 1.38750163408566
109 3.83601 3.66989716831584 1.34366 1.3944722181354
110 3.75153 3.66202818623668 1.35447 1.40138086248782
111 3.78495 3.65414066672283 1.32402 1.40822764475188
112 3.73595 3.64623520050396 1.37583 1.41501264878812
113 3.72531 3.63831237431174 1.32322 1.42173596461323
114 3.73699 3.63037277082024 1.39732 1.42839768830438
115 3.66998 3.62241696858841 1.37475 1.4349979219033
116 3.71147 3.61444554200455 1.40152 1.44153677332012
117 3.64639 3.60645906123285 1.43016 1.44801435623709
118 3.6951 3.59845809216197 1.40445 1.45443079001218
119 3.66645 3.59044319635555 1.49943 1.46078619958258
120 3.64921 3.58241493100476 1.45173 1.46708071536818
121 3.67278 3.57437384888277 1.53109 1.47331447317508
122 3.62043 3.56632049830125 1.50571 1.47948761409904
123 3.70031 3.55825542306865 1.54225 1.48560028442913
124 3.63168 3.55017916245055 1.57811 1.49165263555138
125 3.67956 3.54209225113179 1.54815 1.49764482385267
126 3.64643 3.53399521918047 1.61486 1.50357701062469
127 3.65567 3.52588859201387 1.54865 1.50944936196825
128 3.68399 3.51777289036611 1.61469 1.51526204869769
129 3.62965 3.50964863025763 1.58142 1.52101524624569
130 3.69052 3.50151632296652 1.59474 1.52670913456828
131 3.61013 3.49337647500145 1.60768 1.5323438980503
132 3.65312 3.48522958807652 1.56458 1.53791972541112
133 3.60661 3.47707615908765 1.61913 1.54343680961082
134 3.58948 3.46891668009078 1.55235 1.54889534775679
135 3.60285 3.46075163828164 1.6151 1.55429554101074
136 3.52316 3.45258151597717 1.58248 1.55963759449624
137 3.55328 3.44440679059862 1.60553 1.5649217172067
138 3.46259 3.43622793465615 1.6284 1.57014812191392
139 3.4799 3.42804541573499 1.59773 1.57531702507719
140 3.42929 3.41985969648321 1.67722 1.58042864675293
141 3.38338 3.41167123460091 1.63751 1.58548321050494
142 3.37557 3.40348048283096 1.70799 1.59048094331527
143 3.29446 3.39528788895111 1.68937 1.59542207549571
144 3.32208 3.38709389576762 1.7159 1.60030684059996
145 3.23213 3.37889894111024 1.76005 1.60513547533642
146 3.23184 3.37070345782857 1.72701 1.60990821948175
147 3.18662 3.3625078737898 1.79163 1.61462531579508
148 3.1535 3.35431261187771 1.73895 1.61928700993292
149 3.16797 3.34611808999301 1.78983 1.62389355036493
150 3.10565 3.33792472105498 1.77051 1.6284451882903
151 3.1318 3.32973291300429 1.75787 1.63294217755498
152 3.07723 3.32154306880705 1.77302 1.63738477456971
153 3.11093 3.31335558646008 1.70777 1.6417732382288
154 3.12093 3.30517085899733 1.74051 1.64610782982971
155 3.10783 3.29698927449742 1.68152 1.65038881299353
156 3.15819 3.28881121609233 1.68976 1.65461645358622
157 3.11168 3.28063706197711 1.66027 1.65879101964068
158 3.18533 3.27246718542073 1.61469 1.66291278127977
159 3.16197 3.2643019547779 1.64149 1.66698201064007
160 3.21006 3.25614173350193 1.57209 1.6709989817966
161 3.22436 3.24798688015855 1.6219 1.67496397068835
162 3.21211 3.23983774844071 1.55072 1.67887725504477
163 3.25935 3.23169468718431 1.57393 1.68273911431309
164 3.20934 3.22355804038479 1.55437 1.68654982958656
165 3.26857 3.21542814721467 1.52734 1.69030968353358
166 3.2262 3.20730534204192 1.55872 1.69401896032776
167 3.23214 3.19918995444913 1.51635 1.69767794557889
168 3.23624 3.19108230925351 1.56765 1.70128692626481
169 3.18766 3.18298272652772 1.52781 1.7048461906642
170 3.21679 3.17489152162137 1.5537 1.70835602829033
171 3.15515 3.16680900518334 1.5535 1.71181672982566
172 3.18251 3.15873548318476 1.53893 1.71522858705747
173 3.13452 3.1506712569427 1.58305 1.71859189281433
174 3.12766 3.14261662314453 1.55072 1.72190694090355
175 3.11925 3.13457187387292 1.6005 1.72517402604953
176 3.07438 3.12653729663146 1.58123 1.72839344383307
177 3.09502 3.11851317437089 1.59905 1.7315654906316
178 3.04198 3.11049978551589 1.61886 1.73469046356032
179 3.0635 3.10249740399246 1.62175 1.73776866041431
180 3.03214 3.09450629925579 1.68653 1.74080037961152
181 3.01135 3.0865267363187 1.66188 1.74378592013674
182 3.01369 3.07855897578054 1.71296 1.74672558148643
183 2.97412 3.07060327385655 1.71076 1.74961966361455
184 3.0108 3.06265988240774 1.75704 1.75246846687921
185 2.9509 3.05472904897112 1.79014 1.75527229199036
186 2.94444 3.04681101679041 1.79493 1.75803143995826
187 2.91727 3.03890602484715 1.84866 1.76074621204296
188 2.92702 3.03101430789209 1.82523 1.76341690970464
189 2.93201 3.02313609647712 1.88693 1.76604383455485
190 2.87968 3.01527161698732 1.89585 1.76862728830865
191 2.90914 3.00742109167356 1.91016 1.77116757273766
192 2.86698 2.99958473868522 1.89892 1.77366498962397
193 2.89035 2.99176277210331 1.86992 1.77611984071493
194 2.87119 2.98395540197383 1.90592 1.77853242767887
195 2.86254 2.97616283434137 1.87056 1.78090305206157
196 2.85872 2.96838527128299 1.88602 1.78323201524373
197 2.82335 2.96062291094227 1.8428 1.78551961839921
198 2.86058 2.95287594756363 1.8239 1.78776616245416
199 2.81505 2.94514457152679 1.82252 1.78997194804696
200 2.82796 2.93742896938145 1.7813 1.79213727548902
用OpenLu（可从www.forcal.net下载）求解。
Lu代码：
!!!using["luopt","math","win"]; //使用命名空间
y(yibxilong_infite,yibxilong_s,tao,bet_a,Omega)= yibxilong_infite+((yibxilong_s-yibxilong_infite)/((1+1i*Omega*tao)^bet_a));
目标函数(yibxilong_infite,yibxilong_s,tao,bet_a : i,s, e11,e22 : max, f, e1, e2)=
{
i=-1, s=0, while{++i<max, toreal[y(yibxilong_infite,yibxilong_s,tao,bet_a,f(i,0)), &e11,&e22], s=s+[e1(i,0)-e11]^2+[e2(i,0)-e22]^2 },
s
};
main(: tArray : max, f, e1, e2)=
{
tArray=matrix{ //存放实验数据 //f e1[realPart] e2[imagPart]
"2 4.37523 0.62119
2.08 4.33845 0.62521
。。。省略数据
17.92 2.82796 1.7813"
},
len[tArray,0,&max], f=tArray(all:0), e1=tArray(all:1),e2=tArray(all:2), //用len函数取矩阵的行数，f等取矩阵的列
Opt1[@目标函数] //Opt1函数全局优化
};
结果：
4.169983461876345 3.956371175465695 0.1605626263979209 -0.7356730581825803 23.18657063877461
似乎模型与数据不匹配。
拟合参数：yibxilong_infite,yibxilong_s,tao,bet_a -- 当作复数，匹配度有所提高。
!!!using["luopt","math","win"]; //使用命名空间
y(yibxilong_infite,yibxilong_s,tao,bet_a,Omega)= yibxilong_infite+((yibxilong_s-yibxilong_infite)/((1+1i*Omega*tao)^bet_a));
目标函数(yibxilong_infite,y1,yibxilong_s,y2,tao,t,bet_a,b : i,s, e11,e22 : max, f, e1, e2)=
{
i=-1, s=0, while{++i<max, toreal[y(yibxilong_infite$y1,yibxilong_s$y2,tao$t,bet_a$b,f(i,0)), &e11,&e22], s=s+[e1(i,0)-e11]^2+[e2(i,0)-e22]^2 },
s
};
main(: tArray : max, f, e1, e2)=
{
tArray=matrix{ //存放实验数据 //f e1[realPart] e2[imagPart]
"2 4.37523 0.62119
2.08 4.33845 0.62521
。。。省略数据
17.92 2.82796 1.7813"
},
len[tArray,0,&max], f=tArray(all:0), e1=tArray(all:1),e2=tArray(all:2), //用len函数取矩阵的行数，f等取矩阵的列
Opt1[@目标函数, optwaysimdeep, optwayconfra] //Opt1函数全局优化
};
结果：
3.657474298099148 1.626818043232671 4.318672722366502 0.3945758465293068 -8.820890726323174e-004 8.261459401821658e-002 -0.7118355007158153 2.904333614575917e-003 1.837635420785165
图形：https://blog.csdn.net/wlfc/article/details/119556598