用Lu优化库求解NIST非线性拟合测试题
美国国家标准与技术研究院（NIST: National Institute of Standards and Technology）提供有一套27道非线性拟合测试题，世界上几乎所有著名的数据分析软件包都以能通过该套测试题集为验证标准。下表是Lu求解结果，所有测试题都能以任意随机初始值求得最优解。下表为测试结果：
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测试题1：Misra1a
拟合公式：y =
b1*(1-exp[-b2*x])
拟合参数：b1,b2
数据：y x
（14组）
10.07E0 77.6E0
14.73E0 114.9E0
17.94E0 141.1E0
23.93E0 190.8E0
29.61E0 239.9E0
35.18E0 289.0E0
40.02E0 332.8E0
44.82E0 378.4E0
50.76E0 434.8E0
55.05E0 477.3E0
61.01E0 536.8E0
66.40E0 593.1E0
75.47E0 689.1E0
81.78E0 760.0E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1*(1-exp[-b2*x])-y]^2,
i++
},
s
};
main(::Array,max)=
{
max=14,
Array=new[reals,max,2].SetArray{
"10.07E0 77.6E0
14.73E0 114.9E0
17.94E0 141.1E0
23.93E0 190.8E0
29.61E0 239.9E0
35.18E0 289.0E0
40.02E0 332.8E0
44.82E0 378.4E0
50.76E0 434.8E0
55.05E0 477.3E0
61.01E0 536.8E0
66.40E0 593.1E0
75.47E0 689.1E0
81.78E0 760.0E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
238.9421251876012 5.501564435330968e-004 0.1245513889445084
测试题2：Chwirut2
拟合公式：y =
exp(-b1*x)/(b2+b3*x)
拟合参数：b1,b2,b3
数据：y x
（54组）
92.9000E0 0.500E0
57.1000E0 1.000E0
31.0500E0 1.750E0
11.5875E0 3.750E0
8.0250E0 5.750E0
63.6000E0 0.875E0
21.4000E0 2.250E0
14.2500E0 3.250E0
8.4750E0 5.250E0
63.8000E0 0.750E0
26.8000E0 1.750E0
16.4625E0 2.750E0
7.1250E0 4.750E0
67.3000E0 0.625E0
41.0000E0 1.250E0
21.1500E0 2.250E0
8.1750E0 4.250E0
81.5000E0 .500E0
13.1200E0 3.000E0
59.9000E0 .750E0
14.6200E0 3.000E0
32.9000E0 1.500E0
5.4400E0 6.000E0
12.5600E0 3.000E0
5.4400E0 6.000E0
32.0000E0 1.500E0
13.9500E0 3.000E0
75.8000E0 .500E0
20.0000E0 2.000E0
10.4200E0 4.000E0
59.5000E0 .750E0
21.6700E0 2.000E0
8.5500E0 5.000E0
62.0000E0 .750E0
20.2000E0 2.250E0
7.7600E0 3.750E0
3.7500E0 5.750E0
11.8100E0 3.000E0
54.7000E0 .750E0
23.7000E0 2.500E0
11.5500E0 4.000E0
61.3000E0 .750E0
17.7000E0 2.500E0
8.7400E0 4.000E0
59.2000E0 .750E0
16.3000E0 2.500E0
8.6200E0 4.000E0
81.0000E0 .500E0
4.8700E0 6.000E0
14.6200E0 3.000E0
81.7000E0 .500E0
17.1700E0 2.750E0
81.3000E0 .500E0
28.9000E0 1.750E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ exp(-b1*x)/(b2+b3*x)-y]^2,
i++
},
s
};
main(::Array,max)=
{
max=54,
Array=new[reals,max,2].SetArray{
" 92.9000E0 0.500E0
//数据略
28.9000E0 1.750E0"
},
Opt[@f]
};
结果：
0.1665774524075732 5.165354127091879e-003 1.214996964994243e-002 513.0480294504323
测试题3：Chwirut1
拟合公式：y =
exp[-b1*x]/(b2+b3*x)
拟合参数：b1,b2,b3
数据：y x
（214组）
92.9000E0 0.5000E0
78.7000E0 0.6250E0
64.2000E0 0.7500E0
64.9000E0 0.8750E0
57.1000E0 1.0000E0
43.3000E0 1.2500E0
31.1000E0 1.7500E0
23.6000E0 2.2500E0
31.0500E0 1.7500E0
23.7750E0 2.2500E0
17.7375E0 2.7500E0
13.8000E0 3.2500E0
11.5875E0 3.7500E0
9.4125E0 4.2500E0
7.7250E0 4.7500E0
7.3500E0 5.2500E0
8.0250E0 5.7500E0
90.6000E0 0.5000E0
76.9000E0 0.6250E0
71.6000E0 0.7500E0
63.6000E0 0.8750E0
54.0000E0 1.0000E0
39.2000E0 1.2500E0
29.3000E0 1.7500E0
21.4000E0 2.2500E0
29.1750E0 1.7500E0
22.1250E0 2.2500E0
17.5125E0 2.7500E0
14.2500E0 3.2500E0
9.4500E0 3.7500E0
9.1500E0 4.2500E0
7.9125E0 4.7500E0
8.4750E0 5.2500E0
6.1125E0 5.7500E0
80.0000E0 0.5000E0
79.0000E0 0.6250E0
63.8000E0 0.7500E0
57.2000E0 0.8750E0
53.2000E0 1.0000E0
42.5000E0 1.2500E0
26.8000E0 1.7500E0
20.4000E0 2.2500E0
26.8500E0 1.7500E0
21.0000E0 2.2500E0
16.4625E0 2.7500E0
12.5250E0 3.2500E0
10.5375E0 3.7500E0
8.5875E0 4.2500E0
7.1250E0 4.7500E0
6.1125E0 5.2500E0
5.9625E0 5.7500E0
74.1000E0 0.5000E0
67.3000E0 0.6250E0
60.8000E0 0.7500E0
55.5000E0 0.8750E0
50.3000E0 1.0000E0
41.0000E0 1.2500E0
29.4000E0 1.7500E0
20.4000E0 2.2500E0
29.3625E0 1.7500E0
21.1500E0 2.2500E0
16.7625E0 2.7500E0
13.2000E0 3.2500E0
10.8750E0 3.7500E0
8.1750E0 4.2500E0
7.3500E0 4.7500E0
5.9625E0 5.2500E0
5.6250E0 5.7500E0
81.5000E0 .5000E0
62.4000E0 .7500E0
32.5000E0 1.5000E0
12.4100E0 3.0000E0
13.1200E0 3.0000E0
15.5600E0 3.0000E0
5.6300E0 6.0000E0
78.0000E0 .5000E0
59.9000E0 .7500E0
33.2000E0 1.5000E0
13.8400E0 3.0000E0
12.7500E0 3.0000E0
14.6200E0 3.0000E0
3.9400E0 6.0000E0
76.8000E0 .5000E0
61.0000E0 .7500E0
32.9000E0 1.5000E0
13.8700E0 3.0000E0
11.8100E0 3.0000E0
13.3100E0 3.0000E0
5.4400E0 6.0000E0
78.0000E0 .5000E0
63.5000E0 .7500E0
33.8000E0 1.5000E0
12.5600E0 3.0000E0
5.6300E0 6.0000E0
12.7500E0 3.0000E0
13.1200E0 3.0000E0
5.4400E0 6.0000E0
76.8000E0 .5000E0
60.0000E0 .7500E0
47.8000E0 1.0000E0
32.0000E0 1.5000E0
22.2000E0 2.0000E0
22.5700E0 2.0000E0
18.8200E0 2.5000E0
13.9500E0 3.0000E0
11.2500E0 4.0000E0
9.0000E0 5.0000E0
6.6700E0 6.0000E0
75.8000E0 .5000E0
62.0000E0 .7500E0
48.8000E0 1.0000E0
35.2000E0 1.5000E0
20.0000E0 2.0000E0
20.3200E0 2.0000E0
19.3100E0 2.5000E0
12.7500E0 3.0000E0
10.4200E0 4.0000E0
7.3100E0 5.0000E0
7.4200E0 6.0000E0
70.5000E0 .5000E0
59.5000E0 .7500E0
48.5000E0 1.0000E0
35.8000E0 1.5000E0
21.0000E0 2.0000E0
21.6700E0 2.0000E0
21.0000E0 2.5000E0
15.6400E0 3.0000E0
8.1700E0 4.0000E0
8.5500E0 5.0000E0
10.1200E0 6.0000E0
78.0000E0 .5000E0
66.0000E0 .6250E0
62.0000E0 .7500E0
58.0000E0 .8750E0
47.7000E0 1.0000E0
37.8000E0 1.2500E0
20.2000E0 2.2500E0
21.0700E0 2.2500E0
13.8700E0 2.7500E0
9.6700E0 3.2500E0
7.7600E0 3.7500E0
5.4400E0 4.2500E0
4.8700E0 4.7500E0
4.0100E0 5.2500E0
3.7500E0 5.7500E0
24.1900E0 3.0000E0
25.7600E0 3.0000E0
18.0700E0 3.0000E0
11.8100E0 3.0000E0
12.0700E0 3.0000E0
16.1200E0 3.0000E0
70.8000E0 .5000E0
54.7000E0 .7500E0
48.0000E0 1.0000E0
39.8000E0 1.5000E0
29.8000E0 2.0000E0
23.7000E0 2.5000E0
29.6200E0 2.0000E0
23.8100E0 2.5000E0
17.7000E0 3.0000E0
11.5500E0 4.0000E0
12.0700E0 5.0000E0
8.7400E0 6.0000E0
80.7000E0 .5000E0
61.3000E0 .7500E0
47.5000E0 1.0000E0
29.0000E0 1.5000E0
24.0000E0 2.0000E0
17.7000E0 2.5000E0
24.5600E0 2.0000E0
18.6700E0 2.5000E0
16.2400E0 3.0000E0
8.7400E0 4.0000E0
7.8700E0 5.0000E0
8.5100E0 6.0000E0
66.7000E0 .5000E0
59.2000E0 .7500E0
40.8000E0 1.0000E0
30.7000E0 1.5000E0
25.7000E0 2.0000E0
16.3000E0 2.5000E0
25.9900E0 2.0000E0
16.9500E0 2.5000E0
13.3500E0 3.0000E0
8.6200E0 4.0000E0
7.2000E0 5.0000E0
6.6400E0 6.0000E0
13.6900E0 3.0000E0
81.0000E0 .5000E0
64.5000E0 .7500E0
35.5000E0 1.5000E0
13.3100E0 3.0000E0
4.8700E0 6.0000E0
12.9400E0 3.0000E0
5.0600E0 6.0000E0
15.1900E0 3.0000E0
14.6200E0 3.0000E0
15.6400E0 3.0000E0
25.5000E0 1.7500E0
25.9500E0 1.7500E0
81.7000E0 .5000E0
61.6000E0 .7500E0
29.8000E0 1.7500E0
29.8100E0 1.7500E0
17.1700E0 2.7500E0
10.3900E0 3.7500E0
28.4000E0 1.7500E0
28.6900E0 1.7500E0
81.3000E0 .5000E0
60.9000E0 .7500E0
16.6500E0 2.7500E0
10.0500E0 3.7500E0
28.9000E0 1.7500E0
28.9500E0 1.7500E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ exp[-b1*x]/(b2+b3*x)-y]^2,
i++
},
s
};
main(::Array,max)=
{
max=214,
Array=new[reals,max,2].SetArray{
"92.9000E0 0.5000E0
//（数据略）
28.9500E0 1.7500E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
0.1902757520340506 6.131347827474484e-003 1.053102614699184e-002 2384.477139692282
测试题4：Lanczos3
拟合公式：y =
b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x)
拟合参数：b1,b2,b3,b4,b5,b6
数据：y x
（24组）
2.5134E+00 0.00000E+00
2.0443E+00 5.00000E-02
1.6684E+00 1.00000E-01
1.3664E+00 1.50000E-01
1.1232E+00 2.00000E-01
0.9269E+00 2.50000E-01
0.7679E+00 3.00000E-01
0.6389E+00 3.50000E-01
0.5338E+00 4.00000E-01
0.4479E+00 4.50000E-01
0.3776E+00 5.00000E-01
0.3197E+00 5.50000E-01
0.2720E+00 6.00000E-01
0.2325E+00 6.50000E-01
0.1997E+00 7.00000E-01
0.1723E+00 7.50000E-01
0.1493E+00 8.00000E-01
0.1301E+00 8.50000E-01
0.1138E+00 9.00000E-01
0.1000E+00 9.50000E-01
0.0883E+00 1.00000E+00
0.0783E+00 1.05000E+00
0.0698E+00 1.10000E+00
0.0624E+00 1.15000E+00
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3, b4, b5, b6 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x)-y]^2,
i++
},
s
};
main(::Array,max)=
{
max=24,
Array=new[reals,max,2].SetArray{
"2.5134E+00 0.00000E+00
//数据略
0.0624E+00 1.15000E+00"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
8.681655093842149e-002 0.954982126028795 0.8440068993182961 2.951594761470194 1.58256932803865 4.986355848701066 1.611719361128556e-008
1.582570926941624 4.986355106397009 8.681592937334866e-002 0.9549786258811206 0.8440059225852472 2.951591261252625 1.611719367596706e-008
0.8440187647438969 2.951622113561807 1.582553538843218 4.98636552657069 8.682047980207035e-002 0.9550034446037781 1.611719367600321e-008
测试题5：Gauss1
拟合公式：y =
b1*exp( -b2*x ) + b3*exp( -((x-b4)^2) / b5^2 ) + b6*exp( -((x-b7)^2) / b8^2 )
拟合参数：b1,b2,b3,b4,b5,b6,b7,b8
数据：y x
（250组）
97.62227 1.000000
97.80724 2.000000
96.62247 3.000000
92.59022 4.000000
91.23869 5.000000
95.32704 6.000000
90.35040 7.000000
89.46235 8.000000
91.72520 9.000000
89.86916 10.000000
86.88076 11.00000
85.94360 12.00000
87.60686 13.00000
86.25839 14.00000
80.74976 15.00000
83.03551 16.00000
88.25837 17.00000
82.01316 18.00000
82.74098 19.00000
83.30034 20.00000
81.27850 21.00000
81.85506 22.00000
80.75195 23.00000
80.09573 24.00000
81.07633 25.00000
78.81542 26.00000
78.38596 27.00000
79.93386 28.00000
79.48474 29.00000
79.95942 30.00000
76.10691 31.00000
78.39830 32.00000
81.43060 33.00000
82.48867 34.00000
81.65462 35.00000
80.84323 36.00000
88.68663 37.00000
84.74438 38.00000
86.83934 39.00000
85.97739 40.00000
91.28509 41.00000
97.22411 42.00000
93.51733 43.00000
94.10159 44.00000
101.91760 45.00000
98.43134 46.00000
110.4214 47.00000
107.6628 48.00000
111.7288 49.00000
116.5115 50.00000
120.7609 51.00000
123.9553 52.00000
124.2437 53.00000
130.7996 54.00000
133.2960 55.00000
130.7788 56.00000
132.0565 57.00000
138.6584 58.00000
142.9252 59.00000
142.7215 60.00000
144.1249 61.00000
147.4377 62.00000
148.2647 63.00000
152.0519 64.00000
147.3863 65.00000
149.2074 66.00000
148.9537 67.00000
144.5876 68.00000
148.1226 69.00000
148.0144 70.00000
143.8893 71.00000
140.9088 72.00000
143.4434 73.00000
139.3938 74.00000
135.9878 75.00000
136.3927 76.00000
126.7262 77.00000
124.4487 78.00000
122.8647 79.00000
113.8557 80.00000
113.7037 81.00000
106.8407 82.00000
107.0034 83.00000
102.46290 84.00000
96.09296 85.00000
94.57555 86.00000
86.98824 87.00000
84.90154 88.00000
81.18023 89.00000
76.40117 90.00000
67.09200 91.00000
72.67155 92.00000
68.10848 93.00000
67.99088 94.00000
63.34094 95.00000
60.55253 96.00000
56.18687 97.00000
53.64482 98.00000
53.70307 99.00000
48.07893 100.00000
42.21258 101.00000
45.65181 102.00000
41.69728 103.00000
41.24946 104.00000
39.21349 105.00000
37.71696 106.0000
36.68395 107.0000
37.30393 108.0000
37.43277 109.0000
37.45012 110.0000
32.64648 111.0000
31.84347 112.0000
31.39951 113.0000
26.68912 114.0000
32.25323 115.0000
27.61008 116.0000
33.58649 117.0000
28.10714 118.0000
30.26428 119.0000
28.01648 120.0000
29.11021 121.0000
23.02099 122.0000
25.65091 123.0000
28.50295 124.0000
25.23701 125.0000
26.13828 126.0000
33.53260 127.0000
29.25195 128.0000
27.09847 129.0000
26.52999 130.0000
25.52401 131.0000
26.69218 132.0000
24.55269 133.0000
27.71763 134.0000
25.20297 135.0000
25.61483 136.0000
25.06893 137.0000
27.63930 138.0000
24.94851 139.0000
25.86806 140.0000
22.48183 141.0000
26.90045 142.0000
25.39919 143.0000
17.90614 144.0000
23.76039 145.0000
25.89689 146.0000
27.64231 147.0000
22.86101 148.0000
26.47003 149.0000
23.72888 150.0000
27.54334 151.0000
30.52683 152.0000
28.07261 153.0000
34.92815 154.0000
28.29194 155.0000
34.19161 156.0000
35.41207 157.0000
37.09336 158.0000
40.98330 159.0000
39.53923 160.0000
47.80123 161.0000
47.46305 162.0000
51.04166 163.0000
54.58065 164.0000
57.53001 165.0000
61.42089 166.0000
62.79032 167.0000
68.51455 168.0000
70.23053 169.0000
74.42776 170.0000
76.59911 171.0000
81.62053 172.0000
83.42208 173.0000
79.17451 174.0000
88.56985 175.0000
85.66525 176.0000
86.55502 177.0000
90.65907 178.0000
84.27290 179.0000
85.72220 180.0000
83.10702 181.0000
82.16884 182.0000
80.42568 183.0000
78.15692 184.0000
79.79691 185.0000
77.84378 186.0000
74.50327 187.0000
71.57289 188.0000
65.88031 189.0000
65.01385 190.0000
60.19582 191.0000
59.66726 192.0000
52.95478 193.0000
53.87792 194.0000
44.91274 195.0000
41.09909 196.0000
41.68018 197.0000
34.53379 198.0000
34.86419 199.0000
33.14787 200.0000
29.58864 201.0000
27.29462 202.0000
21.91439 203.0000
19.08159 204.0000
24.90290 205.0000
19.82341 206.0000
16.75551 207.0000
18.24558 208.0000
17.23549 209.0000
16.34934 210.0000
13.71285 211.0000
14.75676 212.0000
13.97169 213.0000
12.42867 214.0000
14.35519 215.0000
7.703309 216.0000
10.234410 217.0000
11.78315 218.0000
13.87768 219.0000
4.535700 220.0000
10.059280 221.0000
8.424824 222.0000
10.533120 223.0000
9.602255 224.0000
7.877514 225.0000
6.258121 226.0000
8.899865 227.0000
7.877754 228.0000
12.51191 229.0000
10.66205 230.0000
6.035400 231.0000
6.790655 232.0000
8.783535 233.0000
4.600288 234.0000
8.400915 235.0000
7.216561 236.0000
10.017410 237.0000
7.331278 238.0000
6.527863 239.0000
2.842001 240.0000
10.325070 241.0000
4.790995 242.0000
8.377101 243.0000
6.264445 244.0000
2.706213 245.0000
8.362329 246.0000
8.983658 247.0000
3.362571 248.0000
1.182746 249.0000
4.875359 250.0000
Lu代码：
!!!using["luopt","math"];
init(::xArray,yArray,Array,max)=
{
global[true], //仅此例使用全局对象（模块变量Array、yArray和xArray分别指向三个全局对象）
max=250,
Array=matrix[max,2].SetArray{
"97.62227 1.000000
//数据略
4.875359 250.0000"
},
yArray=Array(all:0), xArray=Array(all:1) //获得列向量
};
b(x,y :: b1, b2, b3, b4, b5, b6, b7, b8) = [ b1*exp( -b2*x ) + b3*exp( -((x-b4)^2) / b5^2 ) + b6*exp( -((x-b7)^2) / b8^2 )-y]^2;
f(bb1, bb2, bb3, bb4, bb5, bb6, bb7, bb8 :: b1, b2, b3, b4, b5, b6, b7, b8, xArray,yArray) =
b1=bb1, b2=bb2, b3=bb3, b4=bb4, b5=bb5, b6=bb6, b7=bb7, b8=bb8,
arrayfun[@b, xArray, yArray].sum[];
Opt[@f, optmax,200, optmode,100, optdeep,50, optwaysimdeep, optwayconfra, optrange,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5];
结果（前面的数是最优参数，最后一个数是残差平方和）：
98.7782108206542 1.049727649314702e-002 71.99450298346521 178.998050220382 18.38938900582635 100.4899061252454 67.48111125392835 -23.12977338278567 1315.822243203378
98.77821090831286 1.04972765046936e-002 71.99450305835485 178.9980502454348 -18.3893889989036 100.4899063297307 67.48111129662883 23.12977332415829 1315.822243203378
测试题6：Gauss2
拟合公式：y =
b1*exp( -b2*x ) + b3*exp( -((x-b4)^2) / b5^2 ) + b6*exp( -((x-b7)^2) / b8^2 )
拟合参数：b1,b2,b3,b4,b5,b6,b7,b8
数据：y x
（250组）
97.58776 1.000000
97.76344 2.000000
96.56705 3.000000
92.52037 4.000000
91.15097 5.000000
95.21728 6.000000
90.21355 7.000000
89.29235 8.000000
91.51479 9.000000
89.60966 10.000000
86.56187 11.00000
85.55316 12.00000
87.13054 13.00000
85.67940 14.00000
80.04851 15.00000
82.18925 16.00000
87.24081 17.00000
80.79407 18.00000
81.28570 19.00000
81.56940 20.00000
79.22715 21.00000
79.43275 22.00000
77.90195 23.00000
76.75468 24.00000
77.17377 25.00000
74.27348 26.00000
73.11900 27.00000
73.84826 28.00000
72.47870 29.00000
71.92292 30.00000
66.92176 31.00000
67.93835 32.00000
69.56207 33.00000
69.07066 34.00000
66.53983 35.00000
63.87883 36.00000
69.71537 37.00000
63.60588 38.00000
63.37154 39.00000
60.01835 40.00000
62.67481 41.00000
65.80666 42.00000
59.14304 43.00000
56.62951 44.00000
61.21785 45.00000
54.38790 46.00000
62.93443 47.00000
56.65144 48.00000
57.13362 49.00000
58.29689 50.00000
58.91744 51.00000
58.50172 52.00000
55.22885 53.00000
58.30375 54.00000
57.43237 55.00000
51.69407 56.00000
49.93132 57.00000
53.70760 58.00000
55.39712 59.00000
52.89709 60.00000
52.31649 61.00000
53.98720 62.00000
53.54158 63.00000
56.45046 64.00000
51.32276 65.00000
53.11676 66.00000
53.28631 67.00000
49.80555 68.00000
54.69564 69.00000
56.41627 70.00000
54.59362 71.00000
54.38520 72.00000
60.15354 73.00000
59.78773 74.00000
60.49995 75.00000
65.43885 76.00000
60.70001 77.00000
63.71865 78.00000
67.77139 79.00000
64.70934 80.00000
70.78193 81.00000
70.38651 82.00000
77.22359 83.00000
79.52665 84.00000
80.13077 85.00000
85.67823 86.00000
85.20647 87.00000
90.24548 88.00000
93.61953 89.00000
95.86509 90.00000
93.46992 91.00000
105.8137 92.00000
107.8269 93.00000
114.0607 94.00000
115.5019 95.00000
118.5110 96.00000
119.6177 97.00000
122.1940 98.00000
126.9903 99.00000
125.7005 100.00000
123.7447 101.00000
130.6543 102.00000
129.7168 103.00000
131.8240 104.00000
131.8759 105.00000
131.9994 106.0000
132.1221 107.0000
133.4414 108.0000
133.8252 109.0000
133.6695 110.0000
128.2851 111.0000
126.5182 112.0000
124.7550 113.0000
118.4016 114.0000
122.0334 115.0000
115.2059 116.0000
118.7856 117.0000
110.7387 118.0000
110.2003 119.0000
105.17290 120.0000
103.44720 121.0000
94.54280 122.0000
94.40526 123.0000
94.57964 124.0000
88.76605 125.0000
87.28747 126.0000
92.50443 127.0000
86.27997 128.0000
82.44307 129.0000
80.47367 130.0000
78.36608 131.0000
78.74307 132.0000
76.12786 133.0000
79.13108 134.0000
76.76062 135.0000
77.60769 136.0000
77.76633 137.0000
81.28220 138.0000
79.74307 139.0000
81.97964 140.0000
80.02952 141.0000
85.95232 142.0000
85.96838 143.0000
79.94789 144.0000
87.17023 145.0000
90.50992 146.0000
93.23373 147.0000
89.14803 148.0000
93.11492 149.0000
90.34337 150.0000
93.69421 151.0000
95.74256 152.0000
91.85105 153.0000
96.74503 154.0000
87.60996 155.0000
90.47012 156.0000
88.11690 157.0000
85.70673 158.0000
85.01361 159.0000
78.53040 160.0000
81.34148 161.0000
75.19295 162.0000
72.66115 163.0000
69.85504 164.0000
66.29476 165.0000
63.58502 166.0000
58.33847 167.0000
57.50766 168.0000
52.80498 169.0000
50.79319 170.0000
47.03490 171.0000
46.47090 172.0000
43.09016 173.0000
34.11531 174.0000
39.28235 175.0000
32.68386 176.0000
30.44056 177.0000
31.98932 178.0000
23.63330 179.0000
23.69643 180.0000
20.26812 181.0000
19.07074 182.0000
17.59544 183.0000
16.08785 184.0000
18.94267 185.0000
18.61354 186.0000
17.25800 187.0000
16.62285 188.0000
13.48367 189.0000
15.37647 190.0000
13.47208 191.0000
15.96188 192.0000
12.32547 193.0000
16.33880 194.0000
10.438330 195.0000
9.628715 196.0000
13.12268 197.0000
8.772417 198.0000
11.76143 199.0000
12.55020 200.0000
11.33108 201.0000
11.20493 202.0000
7.816916 203.0000
6.800675 204.0000
14.26581 205.0000
10.66285 206.0000
8.911574 207.0000
11.56733 208.0000
11.58207 209.0000
11.59071 210.0000
9.730134 211.0000
11.44237 212.0000
11.22912 213.0000
10.172130 214.0000
12.50905 215.0000
6.201493 216.0000
9.019605 217.0000
10.80607 218.0000
13.09625 219.0000
3.914271 220.0000
9.567886 221.0000
8.038448 222.0000
10.231040 223.0000
9.367410 224.0000
7.695971 225.0000
6.118575 226.0000
8.793207 227.0000
7.796692 228.0000
12.45065 229.0000
10.61601 230.0000
6.001003 231.0000
6.765098 232.0000
8.764653 233.0000
4.586418 234.0000
8.390783 235.0000
7.209202 236.0000
10.012090 237.0000
7.327461 238.0000
6.525136 239.0000
2.840065 240.0000
10.323710 241.0000
4.790035 242.0000
8.376431 243.0000
6.263980 244.0000
2.705892 245.0000
8.362109 246.0000
8.983507 247.0000
3.362469 248.0000
1.182678 249.0000
4.875312 250.0000
Lu代码：
!!!using["luopt","math"];
b(x,y :: b1, b2, b3, b4, b5, b6, b7, b8) = [ b1*exp( -b2*x ) + b3*exp( -((x-b4)^2) / b5^2 ) + b6*exp( -((x-b7)^2) / b8^2 )-y]^2;
f(bb1, bb2, bb3, bb4, bb5, bb6, bb7, bb8 :: b1, b2, b3, b4, b5, b6, b7, b8, xArray,yArray) =
b1=bb1, b2=bb2, b3=bb3, b4=bb4, b5=bb5, b6=bb6, b7=bb7, b8=bb8,
arrayfun[@b, xArray, yArray].sum[];
main(:Array,max:xArray,yArray)=
{
max=250,
Array=matrix[max,2].SetArray{
"97.58776 1.000000
//数据略
4.875312 250.0000"
},
yArray=Array(all:0), xArray=Array(all:1),
Opt[@f, optmax,200, optmode,200, optdeep,50, optwaysimdeep, optwayconfra, optrange,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5]
};
Opt结果（前面的数是最优参数，最后一个数是残差平方和）：
99.01832841148536 1.099494539787634e-002 72.04558941706766 153.2701019520031 19.52597266384597 101.880225315882 107.0309551786922 -23.57858401612744 1247.528209230999
测试题7：DanWood
拟合公式：y =
b1*x^b2
拟合参数：b1,b2
数据：y x
（6组）
2.138E0 1.309E0
3.421E0 1.471E0
3.597E0 1.490E0
4.340E0 1.565E0
4.882E0 1.611E0
5.660E0 1.680E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1*x^b2-y]^2,
i++
},
s
};
main(::Array,max)=
{
max=6,
Array=new[reals,max,2].SetArray{
"2.138E0 1.309E0
3.421E0 1.471E0
3.597E0 1.490E0
4.340E0 1.565E0
4.882E0 1.611E0
5.660E0 1.680E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
0.768862329026337 3.86040541388014 4.317308408310958e-003
测试题8：Misra1b
拟合公式：y =
b1 * (1-(1+b2*x/2)^(-2))
拟合参数：b1,b2
数据：y x
（14组）
Lu代码：
!!!using["luopt","math"];
f(b1, b2 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1 * (1-(1+b2*x/2)^(-2))-y]^2,
i++
},
s
};
main(::Array,max)=
{
max=14,
Array=new[reals,max,2].SetArray{
"10.07E0 77.6E0
14.73E0 114.9E0
17.94E0 141.1E0
23.93E0 190.8E0
29.61E0 239.9E0
35.18E0 289.0E0
40.02E0 332.8E0
44.82E0 378.4E0
50.76E0 434.8E0
55.05E0 477.3E0
61.01E0 536.8E0
66.40E0 593.1E0
75.47E0 689.1E0
81.78E0 760.0E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
337.9974781086999 3.903908913457911e-004 7.546468153364694e-002
测试题9：Kirby2
拟合公式：y =
(b1 + b2*x + b3*x^2) / (1 + b4*x + b5*x^2)
拟合参数：b1,b2,b3,b4,b5
数据：y x
（151组）
0.0082E0 9.65E0
0.0112E0 10.74E0
0.0149E0 11.81E0
0.0198E0 12.88E0
0.0248E0 14.06E0
0.0324E0 15.28E0
0.0420E0 16.63E0
0.0549E0 18.19E0
0.0719E0 19.88E0
0.0963E0 21.84E0
0.1291E0 24.00E0
0.1710E0 26.25E0
0.2314E0 28.86E0
0.3227E0 31.85E0
0.4809E0 35.79E0
0.7084E0 40.18E0
1.0220E0 44.74E0
1.4580E0 49.53E0
1.9520E0 53.94E0
2.5410E0 58.29E0
3.2230E0 62.63E0
3.9990E0 67.03E0
4.8520E0 71.25E0
5.7320E0 75.22E0
6.7270E0 79.33E0
7.8350E0 83.56E0
9.0250E0 87.75E0
10.2670E0 91.93E0
11.5780E0 96.10E0
12.9440E0 100.28E0
14.3770E0 104.46E0
15.8560E0 108.66E0
17.3310E0 112.71E0
18.8850E0 116.88E0
20.5750E0 121.33E0
22.3200E0 125.79E0
22.3030E0 125.79E0
23.4600E0 128.74E0
24.0600E0 130.27E0
25.2720E0 133.33E0
25.8530E0 134.79E0
27.1100E0 137.93E0
27.6580E0 139.33E0
28.9240E0 142.46E0
29.5110E0 143.90E0
30.7100E0 146.91E0
31.3500E0 148.51E0
32.5200E0 151.41E0
33.2300E0 153.17E0
34.3300E0 155.97E0
35.0600E0 157.76E0
36.1700E0 160.56E0
36.8400E0 162.30E0
38.0100E0 165.21E0
38.6700E0 166.90E0
39.8700E0 169.92E0
40.0300E0 170.32E0
40.5000E0 171.54E0
41.3700E0 173.79E0
41.6700E0 174.57E0
42.3100E0 176.25E0
42.7300E0 177.34E0
43.4600E0 179.19E0
44.1400E0 181.02E0
44.5500E0 182.08E0
45.2200E0 183.88E0
45.9200E0 185.75E0
46.3000E0 186.80E0
47.0000E0 188.63E0
47.6800E0 190.45E0
48.0600E0 191.48E0
48.7400E0 193.35E0
49.4100E0 195.22E0
49.7600E0 196.23E0
50.4300E0 198.05E0
51.1100E0 199.97E0
51.5000E0 201.06E0
52.1200E0 202.83E0
52.7600E0 204.69E0
53.1800E0 205.86E0
53.7800E0 207.58E0
54.4600E0 209.50E0
54.8300E0 210.65E0
55.4000E0 212.33E0
56.4300E0 215.43E0
57.0300E0 217.16E0
58.0000E0 220.21E0
58.6100E0 221.98E0
59.5800E0 225.06E0
60.1100E0 226.79E0
61.1000E0 229.92E0
61.6500E0 231.69E0
62.5900E0 234.77E0
63.1200E0 236.60E0
64.0300E0 239.63E0
64.6200E0 241.50E0
65.4900E0 244.48E0
66.0300E0 246.40E0
66.8900E0 249.35E0
67.4200E0 251.32E0
68.2300E0 254.22E0
68.7700E0 256.24E0
69.5900E0 259.11E0
70.1100E0 261.18E0
70.8600E0 264.02E0
71.4300E0 266.13E0
72.1600E0 268.94E0
72.7000E0 271.09E0
73.4000E0 273.87E0
73.9300E0 276.08E0
74.6000E0 278.83E0
75.1600E0 281.08E0
75.8200E0 283.81E0
76.3400E0 286.11E0
76.9800E0 288.81E0
77.4800E0 291.08E0
78.0800E0 293.75E0
78.6000E0 295.99E0
79.1700E0 298.64E0
79.6200E0 300.84E0
79.8800E0 302.02E0
80.1900E0 303.48E0
80.6600E0 305.65E0
81.2200E0 308.27E0
81.6600E0 310.41E0
82.1600E0 313.01E0
82.5900E0 315.12E0
83.1400E0 317.71E0
83.5000E0 319.79E0
84.0000E0 322.36E0
84.4000E0 324.42E0
84.8900E0 326.98E0
85.2600E0 329.01E0
85.7400E0 331.56E0
86.0700E0 333.56E0
86.5400E0 336.10E0
86.8900E0 338.08E0
87.3200E0 340.60E0
87.6500E0 342.57E0
88.1000E0 345.08E0
88.4300E0 347.02E0
88.8300E0 349.52E0
89.1200E0 351.44E0
89.5400E0 353.93E0
89.8500E0 355.83E0
90.2500E0 358.32E0
90.5500E0 360.20E0
90.9300E0 362.67E0
91.2000E0 364.53E0
91.5500E0 367.00E0
92.2000E0 371.30E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3, b4, b5 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ (b1 + b2*x + b3*x^2) / (1 + b4*x + b5*x^2)-y]^2,
i++
},
s
};
main(::Array,max)=
{
max=151,
Array=new[reals,max,2].SetArray{
"0.0082E0 9.65E0
//数据略
92.2000E0 371.30E0"
},
Opt[@f, optdeep,50]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
1.674511144521595 -0.1392736405667982 2.596112399169998e-003 -1.724193594167487e-003 2.166478545526201e-005 3.90507396584925
测试题10：Hahn1
拟合公式：y =
(b1+b2*x+b3*x^2+b4*x^3) / (1+b5*x+b6*x^2+b7*x^3)
拟合参数：b1,b2,b3,b4,b5,b6,b7
数据：y x
（236组）
.591E0 24.41E0
1.547E0 34.82E0
2.902E0 44.09E0
2.894E0 45.07E0
4.703E0 54.98E0
6.307E0 65.51E0
7.03E0 70.53E0
7.898E0 75.70E0
9.470E0 89.57E0
9.484E0 91.14E0
10.072E0 96.40E0
10.163E0 97.19E0
11.615E0 114.26E0
12.005E0 120.25E0
12.478E0 127.08E0
12.982E0 133.55E0
12.970E0 133.61E0
13.926E0 158.67E0
14.452E0 172.74E0
14.404E0 171.31E0
15.190E0 202.14E0
15.550E0 220.55E0
15.528E0 221.05E0
15.499E0 221.39E0
16.131E0 250.99E0
16.438E0 268.99E0
16.387E0 271.80E0
16.549E0 271.97E0
16.872E0 321.31E0
16.830E0 321.69E0
16.926E0 330.14E0
16.907E0 333.03E0
16.966E0 333.47E0
17.060E0 340.77E0
17.122E0 345.65E0
17.311E0 373.11E0
17.355E0 373.79E0
17.668E0 411.82E0
17.767E0 419.51E0
17.803E0 421.59E0
17.765E0 422.02E0
17.768E0 422.47E0
17.736E0 422.61E0
17.858E0 441.75E0
17.877E0 447.41E0
17.912E0 448.7E0
18.046E0 472.89E0
18.085E0 476.69E0
18.291E0 522.47E0
18.357E0 522.62E0
18.426E0 524.43E0
18.584E0 546.75E0
18.610E0 549.53E0
18.870E0 575.29E0
18.795E0 576.00E0
19.111E0 625.55E0
.367E0 20.15E0
.796E0 28.78E0
0.892E0 29.57E0
1.903E0 37.41E0
2.150E0 39.12E0
3.697E0 50.24E0
5.870E0 61.38E0
6.421E0 66.25E0
7.422E0 73.42E0
9.944E0 95.52E0
11.023E0 107.32E0
11.87E0 122.04E0
12.786E0 134.03E0
14.067E0 163.19E0
13.974E0 163.48E0
14.462E0 175.70E0
14.464E0 179.86E0
15.381E0 211.27E0
15.483E0 217.78E0
15.59E0 219.14E0
16.075E0 262.52E0
16.347E0 268.01E0
16.181E0 268.62E0
16.915E0 336.25E0
17.003E0 337.23E0
16.978E0 339.33E0
17.756E0 427.38E0
17.808E0 428.58E0
17.868E0 432.68E0
18.481E0 528.99E0
18.486E0 531.08E0
19.090E0 628.34E0
16.062E0 253.24E0
16.337E0 273.13E0
16.345E0 273.66E0
16.388E0 282.10E0
17.159E0 346.62E0
17.116E0 347.19E0
17.164E0 348.78E0
17.123E0 351.18E0
17.979E0 450.10E0
17.974E0 450.35E0
18.007E0 451.92E0
17.993E0 455.56E0
18.523E0 552.22E0
18.669E0 553.56E0
18.617E0 555.74E0
19.371E0 652.59E0
19.330E0 656.20E0
0.080E0 14.13E0
0.248E0 20.41E0
1.089E0 31.30E0
1.418E0 33.84E0
2.278E0 39.70E0
3.624E0 48.83E0
4.574E0 54.50E0
5.556E0 60.41E0
7.267E0 72.77E0
7.695E0 75.25E0
9.136E0 86.84E0
9.959E0 94.88E0
9.957E0 96.40E0
11.600E0 117.37E0
13.138E0 139.08E0
13.564E0 147.73E0
13.871E0 158.63E0
13.994E0 161.84E0
14.947E0 192.11E0
15.473E0 206.76E0
15.379E0 209.07E0
15.455E0 213.32E0
15.908E0 226.44E0
16.114E0 237.12E0
17.071E0 330.90E0
17.135E0 358.72E0
17.282E0 370.77E0
17.368E0 372.72E0
17.483E0 396.24E0
17.764E0 416.59E0
18.185E0 484.02E0
18.271E0 495.47E0
18.236E0 514.78E0
18.237E0 515.65E0
18.523E0 519.47E0
18.627E0 544.47E0
18.665E0 560.11E0
19.086E0 620.77E0
0.214E0 18.97E0
0.943E0 28.93E0
1.429E0 33.91E0
2.241E0 40.03E0
2.951E0 44.66E0
3.782E0 49.87E0
4.757E0 55.16E0
5.602E0 60.90E0
7.169E0 72.08E0
8.920E0 85.15E0
10.055E0 97.06E0
12.035E0 119.63E0
12.861E0 133.27E0
13.436E0 143.84E0
14.167E0 161.91E0
14.755E0 180.67E0
15.168E0 198.44E0
15.651E0 226.86E0
15.746E0 229.65E0
16.216E0 258.27E0
16.445E0 273.77E0
16.965E0 339.15E0
17.121E0 350.13E0
17.206E0 362.75E0
17.250E0 371.03E0
17.339E0 393.32E0
17.793E0 448.53E0
18.123E0 473.78E0
18.49E0 511.12E0
18.566E0 524.70E0
18.645E0 548.75E0
18.706E0 551.64E0
18.924E0 574.02E0
19.1E0 623.86E0
0.375E0 21.46E0
0.471E0 24.33E0
1.504E0 33.43E0
2.204E0 39.22E0
2.813E0 44.18E0
4.765E0 55.02E0
9.835E0 94.33E0
10.040E0 96.44E0
11.946E0 118.82E0
12.596E0 128.48E0
13.303E0 141.94E0
13.922E0 156.92E0
14.440E0 171.65E0
14.951E0 190.00E0
15.627E0 223.26E0
15.639E0 223.88E0
15.814E0 231.50E0
16.315E0 265.05E0
16.334E0 269.44E0
16.430E0 271.78E0
16.423E0 273.46E0
17.024E0 334.61E0
17.009E0 339.79E0
17.165E0 349.52E0
17.134E0 358.18E0
17.349E0 377.98E0
17.576E0 394.77E0
17.848E0 429.66E0
18.090E0 468.22E0
18.276E0 487.27E0
18.404E0 519.54E0
18.519E0 523.03E0
19.133E0 612.99E0
19.074E0 638.59E0
19.239E0 641.36E0
19.280E0 622.05E0
19.101E0 631.50E0
19.398E0 663.97E0
19.252E0 646.9E0
19.89E0 748.29E0
20.007E0 749.21E0
19.929E0 750.14E0
19.268E0 647.04E0
19.324E0 646.89E0
20.049E0 746.9E0
20.107E0 748.43E0
20.062E0 747.35E0
20.065E0 749.27E0
19.286E0 647.61E0
19.972E0 747.78E0
20.088E0 750.51E0
20.743E0 851.37E0
20.83E0 845.97E0
20.935E0 847.54E0
21.035E0 849.93E0
20.93E0 851.61E0
21.074E0 849.75E0
21.085E0 850.98E0
20.935E0 848.23E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3, b4, b5, b6, b7 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ (b1+b2*x+b3*x^2+b4*x^3) / (1+b5*x+b6*x^2+b7*x^3) - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=236,
Array=new[reals,max,2].SetArray{
".591E0 24.41E0
//数据略
20.935E0 848.23E0"
},
Opt[@f, optdeep,50]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
1.077624604840841 -0.1226917694997709 4.086335893830721e-003 -1.426154108558146e-006 -5.761083740949968e-003 2.405360605861336e-004 -1.231394824343227e-007 1.53243828949271
测试题11：Nelson
拟合公式：ln[y]
= b1 - b2*x1 * exp[-b3*x2]
拟合参数：b1,b2,b3
数据：y x1
x2 （128组）
15.00E0 1E0 180E0
17.00E0 1E0 180E0
15.50E0 1E0 180E0
16.50E0 1E0 180E0
15.50E0 1E0 225E0
15.00E0 1E0 225E0
16.00E0 1E0 225E0
14.50E0 1E0 225E0
15.00E0 1E0 250E0
14.50E0 1E0 250E0
12.50E0 1E0 250E0
11.00E0 1E0 250E0
14.00E0 1E0 275E0
13.00E0 1E0 275E0
14.00E0 1E0 275E0
11.50E0 1E0 275E0
14.00E0 2E0 180E0
16.00E0 2E0 180E0
13.00E0 2E0 180E0
13.50E0 2E0 180E0
13.00E0 2E0 225E0
13.50E0 2E0 225E0
12.50E0 2E0 225E0
12.50E0 2E0 225E0
12.50E0 2E0 250E0
12.00E0 2E0 250E0
11.50E0 2E0 250E0
12.00E0 2E0 250E0
13.00E0 2E0 275E0
11.50E0 2E0 275E0
13.00E0 2E0 275E0
12.50E0 2E0 275E0
13.50E0 4E0 180E0
17.50E0 4E0 180E0
17.50E0 4E0 180E0
13.50E0 4E0 180E0
12.50E0 4E0 225E0
12.50E0 4E0 225E0
15.00E0 4E0 225E0
13.00E0 4E0 225E0
12.00E0 4E0 250E0
13.00E0 4E0 250E0
12.00E0 4E0 250E0
13.50E0 4E0 250E0
10.00E0 4E0 275E0
11.50E0 4E0 275E0
11.00E0 4E0 275E0
9.50E0 4E0 275E0
15.00E0 8E0 180E0
15.00E0 8E0 180E0
15.50E0 8E0 180E0
16.00E0 8E0 180E0
13.00E0 8E0 225E0
10.50E0 8E0 225E0
13.50E0 8E0 225E0
14.00E0 8E0 225E0
12.50E0 8E0 250E0
12.00E0 8E0 250E0
11.50E0 8E0 250E0
11.50E0 8E0 250E0
6.50E0 8E0 275E0
5.50E0 8E0 275E0
6.00E0 8E0 275E0
6.00E0 8E0 275E0
18.50E0 16E0 180E0
17.00E0 16E0 180E0
15.30E0 16E0 180E0
16.00E0 16E0 180E0
13.00E0 16E0 225E0
14.00E0 16E0 225E0
12.50E0 16E0 225E0
11.00E0 16E0 225E0
12.00E0 16E0 250E0
12.00E0 16E0 250E0
11.50E0 16E0 250E0
12.00E0 16E0 250E0
6.00E0 16E0 275E0
6.00E0 16E0 275E0
5.00E0 16E0 275E0
5.50E0 16E0 275E0
12.50E0 32E0 180E0
13.00E0 32E0 180E0
16.00E0 32E0 180E0
12.00E0 32E0 180E0
11.00E0 32E0 225E0
9.50E0 32E0 225E0
11.00E0 32E0 225E0
11.00E0 32E0 225E0
11.00E0 32E0 250E0
10.00E0 32E0 250E0
10.50E0 32E0 250E0
10.50E0 32E0 250E0
2.70E0 32E0 275E0
2.70E0 32E0 275E0
2.50E0 32E0 275E0
2.40E0 32E0 275E0
13.00E0 48E0 180E0
13.50E0 48E0 180E0
16.50E0 48E0 180E0
13.60E0 48E0 180E0
11.50E0 48E0 225E0
10.50E0 48E0 225E0
13.50E0 48E0 225E0
12.00E0 48E0 225E0
7.00E0 48E0 250E0
6.90E0 48E0 250E0
8.80E0 48E0 250E0
7.90E0 48E0 250E0
1.20E0 48E0 275E0
1.50E0 48E0 275E0
1.00E0 48E0 275E0
1.50E0 48E0 275E0
13.00E0 64E0 180E0
12.50E0 64E0 180E0
16.50E0 64E0 180E0
16.00E0 64E0 180E0
11.00E0 64E0 225E0
11.50E0 64E0 225E0
10.50E0 64E0 225E0
10.00E0 64E0 225E0
7.27E0 64E0 250E0
7.50E0 64E0 250E0
6.70E0 64E0 250E0
7.60E0 64E0 250E0
1.50E0 64E0 275E0
1.00E0 64E0 275E0
1.20E0 64E0 275E0
1.20E0 64E0 275E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3 : i,s,x1,x2,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x1=Array[i,1], x2=Array[i,2],
s=s+[ b1 - b2*x1 * exp[-b3*x2] - ln[y]]^2,
i++
},
s
};
main(::Array,max)=
{
max=128,
Array=new[reals,max,3].SetArray{
"15.00E0 1E0 180E0
//数据略
1.20E0 64E0 275E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
2.590687463814829 5.618193183649379e-009 -5.770075911619127e-002 3.797683319503694
测试题12：MGH17
拟合公式：y =
b1 + b2*exp[-x*b4] + b3*exp[-x*b5]
拟合参数：b1,b2,b3,b4,b5
数据：y x
（33组）
8.440000E-01 0.000000E+00
9.080000E-01 1.000000E+01
9.320000E-01 2.000000E+01
9.360000E-01 3.000000E+01
9.250000E-01 4.000000E+01
9.080000E-01 5.000000E+01
8.810000E-01 6.000000E+01
8.500000E-01 7.000000E+01
8.180000E-01 8.000000E+01
7.840000E-01 9.000000E+01
7.510000E-01 1.000000E+02
7.180000E-01 1.100000E+02
6.850000E-01 1.200000E+02
6.580000E-01 1.300000E+02
6.280000E-01 1.400000E+02
6.030000E-01 1.500000E+02
5.800000E-01 1.600000E+02
5.580000E-01 1.700000E+02
5.380000E-01 1.800000E+02
5.220000E-01 1.900000E+02
5.060000E-01 2.000000E+02
4.900000E-01 2.100000E+02
4.780000E-01 2.200000E+02
4.670000E-01 2.300000E+02
4.570000E-01 2.400000E+02
4.480000E-01 2.500000E+02
4.380000E-01 2.600000E+02
4.310000E-01 2.700000E+02
4.240000E-01 2.800000E+02
4.200000E-01 2.900000E+02
4.140000E-01 3.000000E+02
4.110000E-01 3.100000E+02
4.060000E-01 3.200000E+02
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3, b4, b5 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1 + b2*exp[-x*b4] + b3*exp[-x*b5]-y]^2,
i++
},
s
};
main(::Array,max)=
{
max=33,
Array=new[reals,max,2].SetArray{
"8.440000E-01 0.000000E+00
//数据略
4.060000E-01 3.200000E+02"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
0.3754100454061854 -1.464681449942943 1.935841167789581 2.212272891716917e-002 1.286752571391631e-002 5.464894698841419e-005
测试题13：Lanczos1
拟合公式：y =
b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x)
拟合参数：b1,b2,b3,b4,b5,b6
数据：y x
（24组）
2.513400000000E+00 0.000000000000E+00
2.044333373291E+00 5.000000000000E-02
1.668404436564E+00 1.000000000000E-01
1.366418021208E+00 1.500000000000E-01
1.123232487372E+00 2.000000000000E-01
9.268897180037E-01 2.500000000000E-01
7.679338563728E-01 3.000000000000E-01
6.388775523106E-01 3.500000000000E-01
5.337835317402E-01 4.000000000000E-01
4.479363617347E-01 4.500000000000E-01
3.775847884350E-01 5.000000000000E-01
3.197393199326E-01 5.500000000000E-01
2.720130773746E-01 6.000000000000E-01
2.324965529032E-01 6.500000000000E-01
1.996589546065E-01 7.000000000000E-01
1.722704126914E-01 7.500000000000E-01
1.493405660168E-01 8.000000000000E-01
1.300700206922E-01 8.500000000000E-01
1.138119324644E-01 9.000000000000E-01
1.000415587559E-01 9.500000000000E-01
8.833209084540E-02 1.000000000000E+00
7.833544019350E-02 1.050000000000E+00
6.976693743449E-02 1.100000000000E+00
6.239312536719E-02 1.150000000000E+00
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3, b4, b5, b6 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x)-y]^2,
i++
},
s
};
main(::Array,max)=
{
max=24,
Array=new[reals,max,2].SetArray{
"2.513400000000E+00 0.000000000000E+00
//数据略
6.239312536719E-02 1.150000000000E+00"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
0.8607000001309713 3.000000000223758 1.557599999843679 5.00000000010852 9.510000002540869e-002 1.000000000116767 1.427942065814816e-025
测试题14：Lanczos2
拟合公式：y =
b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x)
拟合参数：b1,b2,b3,b4,b5,b6
数据：y x
（24组）
2.51340E+00 0.00000E+00
2.04433E+00 5.00000E-02
1.66840E+00 1.00000E-01
1.36642E+00 1.50000E-01
1.12323E+00 2.00000E-01
9.26890E-01 2.50000E-01
7.67934E-01 3.00000E-01
6.38878E-01 3.50000E-01
5.33784E-01 4.00000E-01
4.47936E-01 4.50000E-01
3.77585E-01 5.00000E-01
3.19739E-01 5.50000E-01
2.72013E-01 6.00000E-01
2.32497E-01 6.50000E-01
1.99659E-01 7.00000E-01
1.72270E-01 7.50000E-01
1.49341E-01 8.00000E-01
1.30070E-01 8.50000E-01
1.13812E-01 9.00000E-01
1.00042E-01 9.50000E-01
8.83321E-02 1.00000E+00
7.83354E-02 1.05000E+00
6.97669E-02 1.10000E+00
6.23931E-02 1.15000E+00
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3, b4, b5, b6 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x)-y]^2,
i++
},
s
};
main(::Array,max)=
{
max=24,
Array=new[reals,max,2].SetArray{
"2.51340E+00 0.00000E+00
//数据略
6.23931E-02 1.15000E+00"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
0.8642465393080181 3.007827563654494 1.552902166882985 5.002879526609126 9.625090209069097e-002 1.005732644277281 2.229942818086877e-011
测试题15：Gauss3
拟合公式：y =
b1*exp( -b2*x ) + b3*exp( -((x-b4)^2) / b5^2 ) + b6*exp( -((x-b7)^2) / b8^2 )
拟合参数：b1,b2,b3,b4,b5,b6,b7,b8
数据：y x
（250组）
97.58776 1.000000
97.76344 2.000000
96.56705 3.000000
92.52037 4.000000
91.15097 5.000000
95.21728 6.000000
90.21355 7.000000
89.29235 8.000000
91.51479 9.000000
89.60965 10.000000
86.56187 11.00000
85.55315 12.00000
87.13053 13.00000
85.67938 14.00000
80.04849 15.00000
82.18922 16.00000
87.24078 17.00000
80.79401 18.00000
81.28564 19.00000
81.56932 20.00000
79.22703 21.00000
79.43259 22.00000
77.90174 23.00000
76.75438 24.00000
77.17338 25.00000
74.27296 26.00000
73.11830 27.00000
73.84732 28.00000
72.47746 29.00000
71.92128 30.00000
66.91962 31.00000
67.93554 32.00000
69.55841 33.00000
69.06592 34.00000
66.53371 35.00000
63.87094 36.00000
69.70526 37.00000
63.59295 38.00000
63.35509 39.00000
59.99747 40.00000
62.64843 41.00000
65.77345 42.00000
59.10141 43.00000
56.57750 44.00000
61.15313 45.00000
54.30767 46.00000
62.83535 47.00000
56.52957 48.00000
56.98427 49.00000
58.11459 50.00000
58.69576 51.00000
58.23322 52.00000
54.90490 53.00000
57.91442 54.00000
56.96629 55.00000
51.13831 56.00000
49.27123 57.00000
52.92668 58.00000
54.47693 59.00000
51.81710 60.00000
51.05401 61.00000
52.51731 62.00000
51.83710 63.00000
54.48196 64.00000
49.05859 65.00000
50.52315 66.00000
50.32755 67.00000
46.44419 68.00000
50.89281 69.00000
52.13203 70.00000
49.78741 71.00000
49.01637 72.00000
54.18198 73.00000
53.17456 74.00000
53.20827 75.00000
57.43459 76.00000
51.95282 77.00000
54.20282 78.00000
57.46687 79.00000
53.60268 80.00000
58.86728 81.00000
57.66652 82.00000
63.71034 83.00000
65.24244 84.00000
65.10878 85.00000
69.96313 86.00000
68.85475 87.00000
73.32574 88.00000
76.21241 89.00000
78.06311 90.00000
75.37701 91.00000
87.54449 92.00000
89.50588 93.00000
95.82098 94.00000
97.48390 95.00000
100.86070 96.00000
102.48510 97.00000
105.7311 98.00000
111.3489 99.00000
111.0305 100.00000
110.1920 101.00000
118.3581 102.00000
118.8086 103.00000
122.4249 104.00000
124.0953 105.00000
125.9337 106.0000
127.8533 107.0000
131.0361 108.0000
133.3343 109.0000
135.1278 110.0000
131.7113 111.0000
131.9151 112.0000
132.1107 113.0000
127.6898 114.0000
133.2148 115.0000
128.2296 116.0000
133.5902 117.0000
127.2539 118.0000
128.3482 119.0000
124.8694 120.0000
124.6031 121.0000
117.0648 122.0000
118.1966 123.0000
119.5408 124.0000
114.7946 125.0000
114.2780 126.0000
120.3484 127.0000
114.8647 128.0000
111.6514 129.0000
110.1826 130.0000
108.4461 131.0000
109.0571 132.0000
106.5308 133.0000
109.4691 134.0000
106.8709 135.0000
107.3192 136.0000
106.9000 137.0000
109.6526 138.0000
107.1602 139.0000
108.2509 140.0000
104.96310 141.0000
109.3601 142.0000
107.6696 143.0000
99.77286 144.0000
104.96440 145.0000
106.1376 146.0000
106.5816 147.0000
100.12860 148.0000
101.66910 149.0000
96.44254 150.0000
97.34169 151.0000
96.97412 152.0000
90.73460 153.0000
93.37949 154.0000
82.12331 155.0000
83.01657 156.0000
78.87360 157.0000
74.86971 158.0000
72.79341 159.0000
65.14744 160.0000
67.02127 161.0000
60.16136 162.0000
57.13996 163.0000
54.05769 164.0000
50.42265 165.0000
47.82430 166.0000
42.85748 167.0000
42.45495 168.0000
38.30808 169.0000
36.95794 170.0000
33.94543 171.0000
34.19017 172.0000
31.66097 173.0000
23.56172 174.0000
29.61143 175.0000
23.88765 176.0000
22.49812 177.0000
24.86901 178.0000
17.29481 179.0000
18.09291 180.0000
15.34813 181.0000
14.77997 182.0000
13.87832 183.0000
12.88891 184.0000
16.20763 185.0000
16.29024 186.0000
15.29712 187.0000
14.97839 188.0000
12.11330 189.0000
14.24168 190.0000
12.53824 191.0000
15.19818 192.0000
11.70478 193.0000
15.83745 194.0000
10.035850 195.0000
9.307574 196.0000
12.86800 197.0000
8.571671 198.0000
11.60415 199.0000
12.42772 200.0000
11.23627 201.0000
11.13198 202.0000
7.761117 203.0000
6.758250 204.0000
14.23375 205.0000
10.63876 206.0000
8.893581 207.0000
11.55398 208.0000
11.57221 209.0000
11.58347 210.0000
9.724857 211.0000
11.43854 212.0000
11.22636 213.0000
10.170150 214.0000
12.50765 215.0000
6.200494 216.0000
9.018902 217.0000
10.80557 218.0000
13.09591 219.0000
3.914033 220.0000
9.567723 221.0000
8.038338 222.0000
10.230960 223.0000
9.367358 224.0000
7.695937 225.0000
6.118552 226.0000
8.793192 227.0000
7.796682 228.0000
12.45064 229.0000
10.61601 230.0000
6.001000 231.0000
6.765096 232.0000
8.764652 233.0000
4.586417 234.0000
8.390782 235.0000
7.209201 236.0000
10.012090 237.0000
7.327461 238.0000
6.525136 239.0000
2.840065 240.0000
10.323710 241.0000
4.790035 242.0000
8.376431 243.0000
6.263980 244.0000
2.705892 245.0000
8.362109 246.0000
8.983507 247.0000
3.362469 248.0000
1.182678 249.0000
4.875312 250.0000
Lu代码：
!!!using["luopt","math"];
b(x,y :: b1, b2, b3, b4, b5, b6, b7, b8) = [ b1*exp( -b2*x ) + b3*exp( -((x-b4)^2) / b5^2 ) + b6*exp( -((x-b7)^2) / b8^2 )-y]^2;
f(bb1, bb2, bb3, bb4, bb5, bb6, bb7, bb8 :: b1, b2, b3, b4, b5, b6, b7, b8, xArray,yArray) =
b1=bb1, b2=bb2, b3=bb3, b4=bb4, b5=bb5, b6=bb6, b7=bb7, b8=bb8,
arrayfun[@b, xArray, yArray].sum[];
main(:Array,max:xArray,yArray)=
{
max=250,
Array=matrix[max,2].SetArray{
"97.58776 1.000000
//数据略
4.875312 250.0000"
},
yArray=Array(all:0), xArray=Array(all:1),
Opt[@f, optmax,200, optmode,200, optdeep,50, optwaysimdeep, optwayconfra, optrange,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5]
};
Opt结果（前面的数是最优参数，最后一个数是残差平方和）：
98.94036882476252 1.094587930530207e-002 73.70503147724 147.7616425656988 -19.66822112314423 100.6955309029307 111.6361946134701 -23.30050005716102 1244.484636013157
测试题16：Misra1c
拟合公式：y =
b1 * (1-(1+2*b2*x)^(-.5))
拟合参数：b1,b2
数据：y x
（14组）
10.07E0 77.6E0
14.73E0 114.9E0
17.94E0 141.1E0
23.93E0 190.8E0
29.61E0 239.9E0
35.18E0 289.0E0
40.02E0 332.8E0
44.82E0 378.4E0
50.76E0 434.8E0
55.05E0 477.3E0
61.01E0 536.8E0
66.40E0 593.1E0
75.47E0 689.1E0
81.78E0 760.0E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1 * (1-(1+2*b2*x)^(-.5)) - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=14,
Array=new[reals,max,2].SetArray{
"10.07E0 77.6E0
14.73E0 114.9E0
17.94E0 141.1E0
23.93E0 190.8E0
29.61E0 239.9E0
35.18E0 289.0E0
40.02E0 332.8E0
44.82E0 378.4E0
50.76E0 434.8E0
55.05E0 477.3E0
61.01E0 536.8E0
66.40E0 593.1E0
75.47E0 689.1E0
81.78E0 760.0E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
636.4273426913255 2.081362395494465e-004 4.096683697225745e-002
测试题17：Misra1d
拟合公式：y =
b1*b2*x*((1+b2*x)^(-1))
拟合参数：b1,b2
数据：y x
（14组）
10.07E0 77.6E0
14.73E0 114.9E0
17.94E0 141.1E0
23.93E0 190.8E0
29.61E0 239.9E0
35.18E0 289.0E0
40.02E0 332.8E0
44.82E0 378.4E0
50.76E0 434.8E0
55.05E0 477.3E0
61.01E0 536.8E0
66.40E0 593.1E0
75.47E0 689.1E0
81.78E0 760.0E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1*b2*x*((1+b2*x)^(-1)) - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=14,
Array=new[reals,max,2].SetArray{
"10.07E0 77.6E0
14.73E0 114.9E0
17.94E0 141.1E0
23.93E0 190.8E0
29.61E0 239.9E0
35.18E0 289.0E0
40.02E0 332.8E0
44.82E0 378.4E0
50.76E0 434.8E0
55.05E0 477.3E0
61.01E0 536.8E0
66.40E0 593.1E0
75.47E0 689.1E0
81.78E0 760.0E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
437.3696847133118 3.022732627489623e-004 5.641929528283238e-002
测试题18：Roszman1
拟合公式：y =
b1 - b2*x - atan[b3/(x-b4)]/pi
拟合参数：b1,b2,b3,b4
数据：y x
（25组）
0.252429 -4868.68
0.252141 -4868.09
0.251809 -4867.41
0.297989 -3375.19
0.296257 -3373.14
0.295319 -3372.03
0.339603 -2473.74
0.337731 -2472.35
0.333820 -2469.45
0.389510 -1894.65
0.386998 -1893.40
0.438864 -1497.24
0.434887 -1495.85
0.427893 -1493.41
0.471568 -1208.68
0.461699 -1206.18
0.461144 -1206.04
0.513532 -997.92
0.506641 -996.61
0.505062 -996.31
0.535648 -834.94
0.533726 -834.66
0.568064 -710.03
0.612886 -530.16
0.624169 -464.17
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3, b4 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1 - b2*x - atan[b3/(x-b4)]/pi - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=25,
Array=new[reals,max,2].SetArray{
"0.252429 -4868.68
//数据略
0.624169 -464.17"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
0.2019697355202454 -6.19555774569437e-006 1204.452016582458 -181.3404387765018 4.948484735061148e-004
测试题19：ENSO
拟合公式：y =
b1 + b2*cos( 2*pi*x/12 ) + b3*sin( 2*pi*x/12 ) + b5*cos( 2*pi*x/b4 ) + b6*sin(
2*pi*x/b4 ) + b8*cos( 2*pi*x/b7 ) + b9*sin( 2*pi*x/b7 )
拟合参数：b1,b2,b3,b4,b5,b6,b7,b8,b9
数据：y x
（168组）
12.90000 1.000000
11.30000 2.000000
10.60000 3.000000
11.20000 4.000000
10.90000 5.000000
7.500000 6.000000
7.700000 7.000000
11.70000 8.000000
12.90000 9.000000
14.30000 10.000000
10.90000 11.00000
13.70000 12.00000
17.10000 13.00000
14.00000 14.00000
15.30000 15.00000
8.500000 16.00000
5.700000 17.00000
5.500000 18.00000
7.600000 19.00000
8.600000 20.00000
7.300000 21.00000
7.600000 22.00000
12.70000 23.00000
11.00000 24.00000
12.70000 25.00000
12.90000 26.00000
13.00000 27.00000
10.90000 28.00000
10.400000 29.00000
10.200000 30.00000
8.000000 31.00000
10.90000 32.00000
13.60000 33.00000
10.500000 34.00000
9.200000 35.00000
12.40000 36.00000
12.70000 37.00000
13.30000 38.00000
10.100000 39.00000
7.800000 40.00000
4.800000 41.00000
3.000000 42.00000
2.500000 43.00000
6.300000 44.00000
9.700000 45.00000
11.60000 46.00000
8.600000 47.00000
12.40000 48.00000
10.500000 49.00000
13.30000 50.00000
10.400000 51.00000
8.100000 52.00000
3.700000 53.00000
10.70000 54.00000
5.100000 55.00000
10.400000 56.00000
10.90000 57.00000
11.70000 58.00000
11.40000 59.00000
13.70000 60.00000
14.10000 61.00000
14.00000 62.00000
12.50000 63.00000
6.300000 64.00000
9.600000 65.00000
11.70000 66.00000
5.000000 67.00000
10.80000 68.00000
12.70000 69.00000
10.80000 70.00000
11.80000 71.00000
12.60000 72.00000
15.70000 73.00000
12.60000 74.00000
14.80000 75.00000
7.800000 76.00000
7.100000 77.00000
11.20000 78.00000
8.100000 79.00000
6.400000 80.00000
5.200000 81.00000
12.00000 82.00000
10.200000 83.00000
12.70000 84.00000
10.200000 85.00000
14.70000 86.00000
12.20000 87.00000
7.100000 88.00000
5.700000 89.00000
6.700000 90.00000
3.900000 91.00000
8.500000 92.00000
8.300000 93.00000
10.80000 94.00000
16.70000 95.00000
12.60000 96.00000
12.50000 97.00000
12.50000 98.00000
9.800000 99.00000
7.200000 100.00000
4.100000 101.00000
10.60000 102.00000
10.100000 103.00000
10.100000 104.00000
11.90000 105.00000
13.60000 106.0000
16.30000 107.0000
17.60000 108.0000
15.50000 109.0000
16.00000 110.0000
15.20000 111.0000
11.20000 112.0000
14.30000 113.0000
14.50000 114.0000
8.500000 115.0000
12.00000 116.0000
12.70000 117.0000
11.30000 118.0000
14.50000 119.0000
15.10000 120.0000
10.400000 121.0000
11.50000 122.0000
13.40000 123.0000
7.500000 124.0000
0.6000000 125.0000
0.3000000 126.0000
5.500000 127.0000
5.000000 128.0000
4.600000 129.0000
8.200000 130.0000
9.900000 131.0000
9.200000 132.0000
12.50000 133.0000
10.90000 134.0000
9.900000 135.0000
8.900000 136.0000
7.600000 137.0000
9.500000 138.0000
8.400000 139.0000
10.70000 140.0000
13.60000 141.0000
13.70000 142.0000
13.70000 143.0000
16.50000 144.0000
16.80000 145.0000
17.10000 146.0000
15.40000 147.0000
9.500000 148.0000
6.100000 149.0000
10.100000 150.0000
9.300000 151.0000
5.300000 152.0000
11.20000 153.0000
16.60000 154.0000
15.60000 155.0000
12.00000 156.0000
11.50000 157.0000
8.600000 158.0000
13.80000 159.0000
8.700000 160.0000
8.600000 161.0000
8.600000 162.0000
8.700000 163.0000
12.80000 164.0000
13.20000 165.0000
14.00000 166.0000
13.40000 167.0000
14.80000 168.0000
Lu代码：
!!!using["luopt","math"];
b(x,y :: b1, b2, b3, b4, b5, b6, b7, b8, b9) = [ b1 + b2*cos( 2*pi*x/12 ) + b3*sin( 2*pi*x/12 ) + b5*cos( 2*pi*x/b4 ) + b6*sin( 2*pi*x/b4 ) + b8*cos( 2*pi*x/b7 ) + b9*sin( 2*pi*x/b7 )-y]^2;
f(bb1, bb2, bb3, bb4, bb5, bb6, bb7, bb8, bb9 :: b1, b2, b3, b4, b5, b6, b7, b8, b9, xArray,yArray) =
b1=bb1, b2=bb2, b3=bb3, b4=bb4, b5=bb5, b6=bb6, b7=bb7, b8=bb8, b9=bb9,
arrayfun[@b, xArray, yArray].sum[];
main(:Array,max:xArray,yArray)=
{
max=168,
Array=matrix[max,2].SetArray{
"12.90000 1.000000
//数据略
14.80000 168.0000"
},
yArray=Array(all:0), xArray=Array(all:1),
Opt[@f, optmax,200, optmode,200, optdeep,50, optrange,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5,-1e5,1e5]
};
Opt结果（前面的数是最优参数，最后一个数是残差平方和）：
10.51073327424133 3.07617710474805 0.5328778316273216 26.8874605258247 0.2121620982443962 1.496800968845953 -44.31108915862377 -1.623156568176215 -0.5254963552019657 788.5397892660883
测试题20：MGH09
拟合公式：y =
b1*(x^2+x*b2) / (x^2+x*b3+b4)
拟合参数：b1,b2,b3,b4
数据：y x
（11组）
1.957000E-01 4.000000E+00
1.947000E-01 2.000000E+00
1.735000E-01 1.000000E+00
1.600000E-01 5.000000E-01
8.440000E-02 2.500000E-01
6.270000E-02 1.670000E-01
4.560000E-02 1.250000E-01
3.420000E-02 1.000000E-01
3.230000E-02 8.330000E-02
2.350000E-02 7.140000E-02
2.460000E-02 6.250000E-02
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3, b4 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1*(x^2+x*b2) / (x^2+x*b3+b4) - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=11,
Array=new[reals,max,2].SetArray{
"1.957000E-01 4.000000E+00
1.947000E-01 2.000000E+00
1.735000E-01 1.000000E+00
1.600000E-01 5.000000E-01
8.440000E-02 2.500000E-01
6.270000E-02 1.670000E-01
4.560000E-02 1.250000E-01
3.420000E-02 1.000000E-01
3.230000E-02 8.330000E-02
2.350000E-02 7.140000E-02
2.460000E-02 6.250000E-02"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
0.192807165561357 0.1912859300055901 0.1230590782999055 0.1360636417774124 3.07505604138942e-004
测试题21：Thurber
拟合公式：y =
(b1 + b2*x + b3*x^2 + b4*x^3) / (1 + b5*x + b6*x^2 + b7*x^3)
拟合参数：b1,b2,b3,b4,b5,b6,b7
数据：y x
（37组）
80.574E0 -3.067E0
84.248E0 -2.981E0
87.264E0 -2.921E0
87.195E0 -2.912E0
89.076E0 -2.840E0
89.608E0 -2.797E0
89.868E0 -2.702E0
90.101E0 -2.699E0
92.405E0 -2.633E0
95.854E0 -2.481E0
100.696E0 -2.363E0
101.060E0 -2.322E0
401.672E0 -1.501E0
390.724E0 -1.460E0
567.534E0 -1.274E0
635.316E0 -1.212E0
733.054E0 -1.100E0
759.087E0 -1.046E0
894.206E0 -0.915E0
990.785E0 -0.714E0
1090.109E0 -0.566E0
1080.914E0 -0.545E0
1122.643E0 -0.400E0
1178.351E0 -0.309E0
1260.531E0 -0.109E0
1273.514E0 -0.103E0
1288.339E0 0.010E0
1327.543E0 0.119E0
1353.863E0 0.377E0
1414.509E0 0.790E0
1425.208E0 0.963E0
1421.384E0 1.006E0
1442.962E0 1.115E0
1464.350E0 1.572E0
1468.705E0 1.841E0
1447.894E0 2.047E0
1457.628E0 2.200E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3, b4, b5, b6, b7 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ (b1 + b2*x + b3*x^2 + b4*x^3) / (1 + b5*x + b6*x^2 + b7*x^3) - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=37,
Array=new[reals,max,2].SetArray{
"80.574E0 -3.067E0
//数据略
1457.628E0 2.200E0"
},
Opt[@f, optdeep,50]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
1288.139263213166 1491.082226190368 583.240938810555 75.41716070590326 0.9662979042232989 0.3979742969130663 4.972747920367406e-002 5642.70824596432
测试题22：BoxBod
拟合公式：y =
b1*(1-exp[-b2*x])
拟合参数：b1,b2
数据：y x
（6组）
109 1
149 2
149 3
191 5
213 7
224 10
Lu代码：
!!!using["luopt","math"];
f(b1, b2 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1*(1-exp[-b2*x]) - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=6,
Array=new[reals,max,2].SetArray{
"109 1
149 2
149 3
191 5
213 7
224 10"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
213.8096072620011 0.5472352937572266 1168.008876656894
测试题23：Rat42
拟合公式：y =
b1 / (1+exp[b2-b3*x])
拟合参数：b1,b2,b3
数据：y x
（9组）
8.930E0 9.000E0
10.800E0 14.000E0
18.590E0 21.000E0
22.330E0 28.000E0
39.350E0 42.000E0
56.110E0 57.000E0
61.730E0 63.000E0
64.620E0 70.000E0
67.080E0 79.000E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1 / (1+exp[b2-b3*x]) - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=9,
Array=new[reals,max,2].SetArray{
"8.930E0 9.000E0
10.800E0 14.000E0
18.590E0 21.000E0
22.330E0 28.000E0
39.350E0 42.000E0
56.110E0 57.000E0
61.730E0 63.000E0
64.620E0 70.000E0
67.080E0 79.000E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
72.46222482160258 2.618078653795331 6.735926034476729e-002 8.056522934471696
测试题24：MGH10
拟合公式：y =
b1 * exp[b2/(x+b3)]
拟合参数：b1,b2,b3
数据：y x
（16组）
3.478000E+04 5.000000E+01
2.861000E+04 5.500000E+01
2.365000E+04 6.000000E+01
1.963000E+04 6.500000E+01
1.637000E+04 7.000000E+01
1.372000E+04 7.500000E+01
1.154000E+04 8.000000E+01
9.744000E+03 8.500000E+01
8.261000E+03 9.000000E+01
7.030000E+03 9.500000E+01
6.005000E+03 1.000000E+02
5.147000E+03 1.050000E+02
4.427000E+03 1.100000E+02
3.820000E+03 1.150000E+02
3.307000E+03 1.200000E+02
2.872000E+03 1.250000E+02
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1 * exp[b2/(x+b3)] - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=16,
Array=new[reals,max,2].SetArray{
"3.478000E+04 5.000000E+01
//数据略
2.872000E+03 1.250000E+02"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
5.609628495120159e-003 6181.347525328181 345.2236740892552 87.94585519192405
测试题25：Eckerle4
拟合公式：y =
(b1/b2) * exp[-0.5*((x-b3)/b2)^2]
拟合参数：b1,b2,b3
数据：y x
（35组）
0.0001575E0 400.000000E0
0.0001699E0 405.000000E0
0.0002350E0 410.000000E0
0.0003102E0 415.000000E0
0.0004917E0 420.000000E0
0.0008710E0 425.000000E0
0.0017418E0 430.000000E0
0.0046400E0 435.000000E0
0.0065895E0 436.500000E0
0.0097302E0 438.000000E0
0.0149002E0 439.500000E0
0.0237310E0 441.000000E0
0.0401683E0 442.500000E0
0.0712559E0 444.000000E0
0.1264458E0 445.500000E0
0.2073413E0 447.000000E0
0.2902366E0 448.500000E0
0.3445623E0 450.000000E0
0.3698049E0 451.500000E0
0.3668534E0 453.000000E0
0.3106727E0 454.500000E0
0.2078154E0 456.000000E0
0.1164354E0 457.500000E0
0.0616764E0 459.000000E0
0.0337200E0 460.500000E0
0.0194023E0 462.000000E0
0.0117831E0 463.500000E0
0.0074357E0 465.000000E0
0.0022732E0 470.000000E0
0.0008800E0 475.000000E0
0.0004579E0 480.000000E0
0.0002345E0 485.000000E0
0.0001586E0 490.000000E0
0.0001143E0 495.000000E0
0.0000710E0 500.000000E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ (b1/b2) * exp[-0.5*((x-b3)/b2)^2] - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=35,
Array=new[reals,max,2].SetArray{
"0.0001575E0 400.000000E0
//数据略
0.0000710E0 500.000000E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
1.554384442089446 4.08883223405797 451.5412077854133 1.463588751943442e-003
-1.554382683717856 -4.088836272271331 451.5412206984345 1.463588749382778e-003
测试题26：Rat43
拟合公式：y =
b1 / ((1+exp[b2-b3*x])^(1/b4))
拟合参数：b1,b2,b3,b4
数据：y x
（15组）
16.08E0 1.0E0
33.83E0 2.0E0
65.80E0 3.0E0
97.20E0 4.0E0
191.55E0 5.0E0
326.20E0 6.0E0
386.87E0 7.0E0
520.53E0 8.0E0
590.03E0 9.0E0
651.92E0 10.0E0
724.93E0 11.0E0
699.56E0 12.0E0
689.96E0 13.0E0
637.56E0 14.0E0
717.41E0 15.0E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3, b4 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1 / ((1+exp[b2-b3*x])^(1/b4)) - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=15,
Array=new[reals,max,2].SetArray{
"16.08E0 1.0E0
//数据略
717.41E0 15.0E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
699.6403298589517 5.27732624904101 0.7596471569440603 1.279316273875911 8786.404917453188
测试题27：Bennett5
拟合公式：y =
b1 * (b2+x)^(-1/b3)
拟合参数：b1,b2,b3
数据：y x
（154组）
-34.834702E0 7.447168E0
-34.393200E0 8.102586E0
-34.152901E0 8.452547E0
-33.979099E0 8.711278E0
-33.845901E0 8.916774E0
-33.732899E0 9.087155E0
-33.640301E0 9.232590E0
-33.559200E0 9.359535E0
-33.486801E0 9.472166E0
-33.423100E0 9.573384E0
-33.365101E0 9.665293E0
-33.313000E0 9.749461E0
-33.260899E0 9.827092E0
-33.217400E0 9.899128E0
-33.176899E0 9.966321E0
-33.139198E0 10.029280E0
-33.101601E0 10.088510E0
-33.066799E0 10.144430E0
-33.035000E0 10.197380E0
-33.003101E0 10.247670E0
-32.971298E0 10.295560E0
-32.942299E0 10.341250E0
-32.916302E0 10.384950E0
-32.890202E0 10.426820E0
-32.864101E0 10.467000E0
-32.841000E0 10.505640E0
-32.817799E0 10.542830E0
-32.797501E0 10.578690E0
-32.774300E0 10.613310E0
-32.757000E0 10.646780E0
-32.733799E0 10.679150E0
-32.716400E0 10.710520E0
-32.699100E0 10.740920E0
-32.678799E0 10.770440E0
-32.661400E0 10.799100E0
-32.644001E0 10.826970E0
-32.626701E0 10.854080E0
-32.612202E0 10.880470E0
-32.597698E0 10.906190E0
-32.583199E0 10.931260E0
-32.568699E0 10.955720E0
-32.554298E0 10.979590E0
-32.539799E0 11.002910E0
-32.525299E0 11.025700E0
-32.510799E0 11.047980E0
-32.499199E0 11.069770E0
-32.487598E0 11.091100E0
-32.473202E0 11.111980E0
-32.461601E0 11.132440E0
-32.435501E0 11.152480E0
-32.435501E0 11.172130E0
-32.426800E0 11.191410E0
-32.412300E0 11.210310E0
-32.400799E0 11.228870E0
-32.392101E0 11.247090E0
-32.380501E0 11.264980E0
-32.366001E0 11.282560E0
-32.357300E0 11.299840E0
-32.348598E0 11.316820E0
-32.339901E0 11.333520E0
-32.328400E0 11.349940E0
-32.319698E0 11.366100E0
-32.311001E0 11.382000E0
-32.299400E0 11.397660E0
-32.290699E0 11.413070E0
-32.282001E0 11.428240E0
-32.273300E0 11.443200E0
-32.264599E0 11.457930E0
-32.256001E0 11.472440E0
-32.247299E0 11.486750E0
-32.238602E0 11.500860E0
-32.229900E0 11.514770E0
-32.224098E0 11.528490E0
-32.215401E0 11.542020E0
-32.203800E0 11.555380E0
-32.198002E0 11.568550E0
-32.189400E0 11.581560E0
-32.183601E0 11.594420E0
-32.174900E0 11.607121E0
-32.169102E0 11.619640E0
-32.163300E0 11.632000E0
-32.154598E0 11.644210E0
-32.145901E0 11.656280E0
-32.140099E0 11.668200E0
-32.131401E0 11.679980E0
-32.125599E0 11.691620E0
-32.119801E0 11.703130E0
-32.111198E0 11.714510E0
-32.105400E0 11.725760E0
-32.096699E0 11.736880E0
-32.090900E0 11.747890E0
-32.088001E0 11.758780E0
-32.079300E0 11.769550E0
-32.073502E0 11.780200E0
-32.067699E0 11.790730E0
-32.061901E0 11.801160E0
-32.056099E0 11.811480E0
-32.050301E0 11.821700E0
-32.044498E0 11.831810E0
-32.038799E0 11.841820E0
-32.033001E0 11.851730E0
-32.027199E0 11.861550E0
-32.024300E0 11.871270E0
-32.018501E0 11.880890E0
-32.012699E0 11.890420E0
-32.004002E0 11.899870E0
-32.001099E0 11.909220E0
-31.995300E0 11.918490E0
-31.989500E0 11.927680E0
-31.983700E0 11.936780E0
-31.977900E0 11.945790E0
-31.972099E0 11.954730E0
-31.969299E0 11.963590E0
-31.963501E0 11.972370E0
-31.957701E0 11.981070E0
-31.951900E0 11.989700E0
-31.946100E0 11.998260E0
-31.940300E0 12.006740E0
-31.937401E0 12.015150E0
-31.931601E0 12.023490E0
-31.925800E0 12.031760E0
-31.922899E0 12.039970E0
-31.917101E0 12.048100E0
-31.911301E0 12.056170E0
-31.908400E0 12.064180E0
-31.902599E0 12.072120E0
-31.896900E0 12.080010E0
-31.893999E0 12.087820E0
-31.888201E0 12.095580E0
-31.885300E0 12.103280E0
-31.882401E0 12.110920E0
-31.876600E0 12.118500E0
-31.873699E0 12.126030E0
-31.867901E0 12.133500E0
-31.862101E0 12.140910E0
-31.859200E0 12.148270E0
-31.856300E0 12.155570E0
-31.850500E0 12.162830E0
-31.844700E0 12.170030E0
-31.841801E0 12.177170E0
-31.838900E0 12.184270E0
-31.833099E0 12.191320E0
-31.830200E0 12.198320E0
-31.827299E0 12.205270E0
-31.821600E0 12.212170E0
-31.818701E0 12.219030E0
-31.812901E0 12.225840E0
-31.809999E0 12.232600E0
-31.807100E0 12.239320E0
-31.801300E0 12.245990E0
-31.798401E0 12.252620E0
-31.795500E0 12.259200E0
-31.789700E0 12.265750E0
-31.786800E0 12.272240E0
Lu代码：
!!!using["luopt","math"];
f(b1, b2, b3 : i,s,x,y : Array,max)=
{
s=0,i=0,(i<max).while{
y=Array[i,0], x=Array[i,1],
s=s+[ b1 * (b2+x)^(-1/b3) - y]^2,
i++
},
s
};
main(::Array,max)=
{
max=154,
Array=new[reals,max,2].SetArray{
"-34.834702E0 7.447168E0
//数据略
-31.786800E0 12.272240E0"
},
Opt[@f]
};
结果（前面的数是最优参数，最后一个数是残差平方和）：
-2522.492288490669 46.73232949578085 0.9322540413312467 5.240474843747452e-004
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E-mail: forcal@sina.com
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最近更新:
2012年11月02日