3层网络的结构是
d2（mnist 0,1）-49-30-2-（2*k） ,k∈(0,1)
分类mnist的0和1，三层的节点数分别是49，30，2激活函数用sigmoid。让0向1，0收敛让1向0，1收敛。
5层网络的结构是
d2（mnist 0,1）-81-30-49-30-2-（2*k） ,k∈(0,1)
分类mnist的0和1，五层的节点数分别是81，30，49，30，2激活函数用sigmoid。让0向1，0收敛让1向0，1收敛。
3层一个卷积核的网络的结构是
d2（mnist 0,1）81-con（3*3）49-30-2-（2*k） ,k∈(0,1)
分类mnist的0和1，输入9*9的图片经过一个3*3的卷积核尺寸变成7*7，隐藏层30个节点输出层2个节点。激活函数用sigmoid。让0向1，0收敛让1向0，1收敛。
具体进样顺序
d2（mnist 0,1）81-con（3*3）49-30-2-（2*k） ,k∈(0,1)
对应的网络结构
这个网络的收敛标准是
if (Math.abs(f2[0]-y[0])< δ && Math.abs(f2[1]-y[1])< δ )
| 具体进样顺序 |
|||
| δ=0.5 |
迭代次数 |
||
| minst 0-1 |
1 |
判断是否达到收敛 |
|
| minst 1-1 |
2 |
判断是否达到收敛 |
|
| 梯度下降 |
|||
| minst 0-2 |
3 |
判断是否达到收敛 |
|
| minst 1-2 |
4 |
判断是否达到收敛 |
|
| …… |
|||
| minst 0-4999 |
9997 |
判断是否达到收敛 |
|
| minst 1-4999 |
9998 |
判断是否达到收敛 |
|
| 梯度下降 |
|||
| …… |
|||
| 如果4999图片内没有达到收敛标准再次从头循环 |
|||
| minst 0-1 |
9999 |
判断是否达到收敛 |
|
| minst 1-1 |
10000 |
判断是否达到收敛 |
|
| …… |
|||
| 每当网路达到收敛标准记录迭代次数和对应的准确率测试结果 |
|||
| 将这一过程重复199次 |
|||
| δ=0.01 |
|||
| …… |
|||
| δ=1e-6 |
|||
对应每个收敛标准都要收敛199次取平均值，记录199次的最大值，尝试了从0.5到1e-6共34个收敛标准，所以对应每个网络需收敛199*34次。
通过比较平均准确率，最大准确率，迭代次数，收敛时间比较三个网络的性能
| 3层 |
5层 |
3层1个卷积核 |
3层 |
5层 |
3层1个卷积核 |
|||
| δ |
平均准确率p-ave |
平均准确率p-ave |
平均准确率p-ave |
δ |
最大值p-max |
最大值p-max |
最大值p-max |
|
| 0.5 |
0.556121031 |
0.502438908 |
0.517770888 |
0.5 |
0.941371158 |
0.810874704 |
0.867612293 |
|
| 0.4 |
0.985338038 |
0.906190527 |
0.75949725 |
0.4 |
0.992434988 |
0.997163121 |
0.996690307 |
|
| 0.3 |
0.988685746 |
0.99093339 |
0.914275871 |
0.3 |
0.991489362 |
0.99858156 |
0.998108747 |
|
| 0.2 |
0.988645355 |
0.996196111 |
0.944265061 |
0.2 |
0.990543735 |
0.999054374 |
0.998108747 |
|
| 0.1 |
0.987623698 |
0.996065434 |
0.953538377 |
0.1 |
0.990543735 |
0.999054374 |
0.997635934 |
|
| 0.01 |
0.989999644 |
0.995502334 |
0.928536298 |
0.01 |
0.991489362 |
0.999054374 |
0.998108747 |
|
| 0.001 |
0.968157573 |
0.989992516 |
0.907932095 |
0.001 |
0.992907801 |
1 |
0.99858156 |
|
| 9.00E-04 |
0.968585243 |
0.991353933 |
0.890459389 |
9.00E-04 |
0.992907801 |
1 |
0.99858156 |
|
| 8.00E-04 |
0.972377253 |
0.989754921 |
0.901728501 |
8.00E-04 |
0.992907801 |
0.999527187 |
0.998108747 |
|
| 7.00E-04 |
0.978036756 |
0.990120817 |
0.897024128 |
7.00E-04 |
0.992907801 |
1 |
0.997635934 |
|
| 6.00E-04 |
0.985468715 |
0.99272723 |
0.898440191 |
6.00E-04 |
0.993380615 |
0.999527187 |
0.999527187 |
|
| 5.00E-04 |
0.991769723 |
0.991821994 |
0.881307245 |
5.00E-04 |
0.992907801 |
0.999527187 |
0.998108747 |
|
| 4.00E-04 |
0.992444492 |
0.994896468 |
0.872397448 |
4.00E-04 |
0.993380615 |
0.999527187 |
0.998108747 |
|
| 3.00E-04 |
0.992969576 |
0.995005762 |
0.884989962 |
3.00E-04 |
0.994799054 |
0.999527187 |
0.999054374 |
|
| 2.00E-04 |
0.994399895 |
0.994136165 |
0.887848225 |
2.00E-04 |
0.994799054 |
0.998108747 |
0.998108747 |
|
| 1.00E-04 |
0.993960346 |
0.991389572 |
0.847616332 |
1.00E-04 |
0.994799054 |
1 |
0.999054374 |
|
| 9.00E-05 |
0.993421006 |
0.993760766 |
0.870898226 |
9.00E-05 |
0.994799054 |
0.999527187 |
0.99858156 |
|
| 8.00E-05 |
0.993409126 |
0.994818062 |
0.846815638 |
8.00E-05 |
0.994799054 |
0.999527187 |
0.999527187 |
|
| 7.00E-05 |
0.993663352 |
0.996609525 |
0.858384119 |
7.00E-05 |
0.994799054 |
0.999527187 |
0.99858156 |
|
| 6.00E-05 |
0.99426209 |
0.996367179 |
0.822433681 |
6.00E-05 |
0.994799054 |
0.999527187 |
0.999054374 |
|
| 5.00E-05 |
0.994269218 |
0.992064341 |
0.812162467 |
5.00E-05 |
0.994799054 |
0.999527187 |
0.99858156 |
|
| 4.00E-05 |
0.99394609 |
0.988716633 |
0.833256115 |
4.00E-05 |
0.994799054 |
0.999054374 |
0.999527187 |
|
| 3.00E-05 |
0.993435262 |
0.987889804 |
0.860681659 |
3.00E-05 |
0.993853428 |
0.997163121 |
0.999054374 |
|
| 2.00E-05 |
0.993437638 |
0.990051915 |
0.861140216 |
2.00E-05 |
0.994326241 |
0.996690307 |
0.999054374 |
|
| 1.00E-05 |
0.994366632 |
0.989160935 |
0.868161137 |
1.00E-05 |
0.995271868 |
0.994326241 |
0.998108747 |
|
| 9.00E-06 |
0.994266842 |
0.989065897 |
0.856100835 |
9.00E-06 |
0.994799054 |
0.992907801 |
0.999054374 |
|
| 8.00E-06 |
0.993960346 |
0.989201326 |
0.856908657 |
8.00E-06 |
0.995271868 |
0.992434988 |
0.999054374 |
|
| 7.00E-06 |
0.993810661 |
0.989217957 |
0.851536643 |
7.00E-06 |
0.994799054 |
0.992907801 |
0.999054374 |
|
| 6.00E-06 |
0.994031624 |
0.989120544 |
0.835860152 |
6.00E-06 |
0.994799054 |
0.992434988 |
0.999527187 |
|
| 5.00E-06 |
0.994314361 |
0.988958979 |
0.839217363 |
5.00E-06 |
0.994799054 |
0.992907801 |
0.999527187 |
|
| 4.00E-06 |
0.993972225 |
0.988844934 |
0.82416337 |
4.00E-06 |
0.994799054 |
0.994799054 |
0.999054374 |
|
| 3.00E-06 |
0.993385367 |
0.989270228 |
0.84103021 |
3.00E-06 |
0.993853428 |
0.994799054 |
0.999054374 |
|
| 2.00E-06 |
0.993836796 |
0.99128503 |
0.857692719 |
2.00E-06 |
0.993853428 |
0.998108747 |
0.999054374 |
|
| 1.00E-06 |
0.993896195 |
0.991068819 |
0.887536976 |
1.00E-06 |
0.994326241 |
0.99858156 |
0.999054374 |
|
199次平均准确率p-ave
| δ=1e-6 |
3层 |
> |
5层 |
> |
3层1个卷积核 |
| 1.11983638 |
1.116650738 |
1 |
也就是49-30-2的网络结构上加一个3*3的核导致平均性能下降，
在49-30-2的结构上加两层81*30也会导致平均性能下降，但要好于加卷积核。
199次的最大准确率p-max
| δ=1e-6 |
3层1个卷积核 |
> |
5层 |
> |
3层 |
| 1.004755112 |
1.0042796 |
1 |
在49-30-2的基础上加一个卷积核非常明显的提高了网络的最大分辨率。在49-30-2的基础上加两层81-30也可以提高分辨率但是没有加卷积核效果好。
综合前两个表
加卷积核和加层数确实都会使最大性能上升，而且针对测试的3个网络加卷积核的效果更明显。
| 3层 |
5层 |
3层1个卷积核 |
3层 |
5层 |
3层1个卷积核 |
|||
| δ |
迭代次数n |
迭代次数n |
迭代次数n |
δ |
耗时 min/199 |
耗时 min/199 |
耗时 min/199 |
|
| 0.5 |
9.457286432 |
17.31155779 |
16.48241206 |
0.5 |
4.0988 |
4.0001 |
2.255266667 |
|
| 0.4 |
217.8743719 |
3767.874372 |
1361.045226 |
0.4 |
4.409 |
17.10336667 |
3.560683333 |
|
| 0.3 |
282.9095477 |
3700.497487 |
1720.190955 |
0.3 |
4.53095 |
16.88956667 |
3.8671 |
|
| 0.2 |
359.3015075 |
3768.60804 |
1943.336683 |
0.2 |
4.715983333 |
20.48565 |
4.079916667 |
|
| 0.1 |
440.5829146 |
3784.150754 |
2060.557789 |
0.1 |
1.64245 |
22.22303333 |
4.198666667 |
|
| 0.01 |
898.4974874 |
3937.753769 |
2972.638191 |
0.01 |
5.714416667 |
22.55365 |
5.0917 |
|
| 0.001 |
1712.040201 |
4656.899497 |
4091.859296 |
0.001 |
7.16 |
25.62353333 |
6.158 |
|
| 9.00E-04 |
1735.075377 |
4732.698492 |
4177.100503 |
9.00E-04 |
7.092683333 |
26.34898333 |
6.25685 |
|
| 8.00E-04 |
1817.145729 |
4773.040201 |
4204.231156 |
8.00E-04 |
7.23665 |
26.55536667 |
6.278333333 |
|
| 7.00E-04 |
1967.728643 |
4867.150754 |
4327.341709 |
7.00E-04 |
7.516983333 |
28.6609 |
6.3886 |
|
| 6.00E-04 |
2466.407035 |
5051.743719 |
4319.231156 |
6.00E-04 |
8.460533333 |
25.1193 |
6.332716667 |
|
| 5.00E-04 |
2922.668342 |
5123.552764 |
4640.180905 |
5.00E-04 |
9.2511 |
28.59831667 |
6.635683333 |
|
| 4.00E-04 |
2991.839196 |
5354.537688 |
4781.437186 |
4.00E-04 |
9.083083333 |
27.10175 |
6.873116667 |
|
| 3.00E-04 |
4905.969849 |
5771.562814 |
4958.276382 |
3.00E-04 |
12.79875 |
31.4649 |
7.17385 |
|
| 2.00E-04 |
5184 |
6608.065327 |
5410.175879 |
2.00E-04 |
13.1458 |
33.98488333 |
7.57195 |
|
| 1.00E-04 |
5632.281407 |
9968.100503 |
5985.060302 |
1.00E-04 |
14.19561667 |
49.20433333 |
9.430166667 |
|
| 9.00E-05 |
5723.949749 |
10986.21608 |
5960.879397 |
9.00E-05 |
14.03463333 |
53.35168333 |
7.974133333 |
|
| 8.00E-05 |
5754.090452 |
11628.0201 |
6234.261307 |
8.00E-05 |
14.26598333 |
56.16275 |
9.804233333 |
|
| 7.00E-05 |
6029.919598 |
12679.52764 |
6227.025126 |
7.00E-05 |
14.79435 |
61.09628333 |
9.777633333 |
|
| 6.00E-05 |
6686.894472 |
13109.78894 |
6410.015075 |
6.00E-05 |
15.92916667 |
63.26216667 |
10.05916667 |
|
| 5.00E-05 |
6733.869347 |
13711.12563 |
6843.527638 |
5.00E-05 |
15.9815 |
65.85913333 |
10.4674 |
|
| 4.00E-05 |
7164.261307 |
15135.42211 |
7226.582915 |
4.00E-05 |
17.0012 |
71.99621667 |
10.87256667 |
|
| 3.00E-05 |
7876 |
20739.68844 |
7567.170854 |
3.00E-05 |
18.42911667 |
97.38201667 |
11.22465 |
|
| 2.00E-05 |
7898.773869 |
33675.49246 |
8543.718593 |
2.00E-05 |
18.3598 |
134.5200333 |
12.26171667 |
|
| 1.00E-05 |
10519.22613 |
56278.17085 |
10002.80905 |
1.00E-05 |
20.59985 |
249.1469333 |
13.94281667 |
|
| 9.00E-06 |
10562.28141 |
59442.55276 |
10314.70854 |
9.00E-06 |
23.99378333 |
262.6644167 |
14.27883333 |
|
| 8.00E-06 |
10718.19095 |
62130.47739 |
10521.40704 |
8.00E-06 |
24.3037 |
276.2772 |
14.46355 |
|
| 7.00E-06 |
10886.38191 |
66274.23618 |
10795.59296 |
7.00E-06 |
23.87303333 |
232.7295667 |
15.33916667 |
|
| 6.00E-06 |
11353.8392 |
70016.88442 |
11356.43216 |
6.00E-06 |
25.2186 |
243.7804833 |
16.13403333 |
|
| 5.00E-06 |
13691.06533 |
75014.68844 |
11524.1407 |
5.00E-06 |
29.18276667 |
262.0030333 |
16.73851667 |
|
| 4.00E-06 |
16101.14573 |
81911.47739 |
12755.88945 |
4.00E-06 |
30.34141667 |
283.9857167 |
18.1965 |
|
| 3.00E-06 |
16729.59799 |
92364.45729 |
13319.76884 |
3.00E-06 |
34.85281667 |
319.9373 |
18.90775 |
|
| 2.00E-06 |
17837.20603 |
113501.8392 |
15362.49749 |
2.00E-06 |
32.95221667 |
392.8333667 |
21.25796667 |
|
| 1.00E-06 |
20745.63819 |
154350.5126 |
17225.44221 |
1.00E-06 |
41.03785 |
559.2056333 |
23.45475 |
迭代次数
| δ=1e-6 |
5层 |
> |
3层 |
> |
3层一个卷积核 |
| 8.96061249 |
1.204360268 |
1 |
5层网络的迭代次数要远大于其他的两个网络。
耗时
| δ=1e-6 |
5层 |
> |
3层 |
> |
3层一个卷积核 |
| 23.84189272 |
1.749660517 |
1 |
5层网络消耗了23倍的时间取得的最大准确率仍小于3层一个卷积核的网络。
199次平均准确率p-ave
3层>5层>3层1个卷积核
199次的最大准确率p-max
3层1个卷积核>5层>3层
迭代次数
5层>3层>3层一个卷积核
耗时
5层>3层>3层一个卷积核
所以针对测试的3个网络在49-30-2的基础上加两层81-30会使网络的平均性能基本不变的情况下最大性能显著提升，但代价是收敛速度严重下降。
在49-30-2的基础上增加卷积核会大幅提升网络的最大性能，但卷积核太少了会影响网络的平均性能，使性能变得不稳定。
| 实验数据 |
| 学习率 0.1 |
| 权重初始化方式 |
| Random rand1 =new Random(); |
| int ti1=rand1.nextInt(98)+1; |
| int xx=1; |
| if(ti1%2==0) |
| { xx=-1;} |
| tw[a][b]=xx*((double)ti1/x); |
| 第一层第二层和卷积核的权重的初始化的x分别为1000,1000,200 |
3层网络的2层权重x=1000
5层网络的4层权重x=1000
3层1个卷积核网络的2层权重x=1000，卷积核的x=200
| 49-30-2 |
||||||||
| f2[0] |
f2[1] |
迭代次数n |
平均准确率p-ave |
δ |
耗时ms/次 |
耗时ms/199次 |
耗时 min/199 |
最大值p-max |
| 0.499386185 |
0.499447667 |
9.457286432 |
0.556121031 |
0.5 |
1235.738693 |
245928 |
4.0988 |
0.941371158 |
| 0.607789346 |
0.391697375 |
217.8743719 |
0.985338038 |
0.4 |
1329.266332 |
264540 |
4.409 |
0.992434988 |
| 0.717279441 |
0.282649246 |
282.9095477 |
0.988685746 |
0.3 |
1366.035176 |
271857 |
4.53095 |
0.991489362 |
| 0.80371583 |
0.196464405 |
359.3015075 |
0.988645355 |
0.2 |
1421.899497 |
282959 |
4.715983333 |
0.990543735 |
| 0.845146872 |
0.154991397 |
440.5829146 |
0.987623698 |
0.1 |
495.1306533 |
98547 |
1.64245 |
0.990543735 |
| 0.081706915 |
0.918288828 |
898.4974874 |
0.989999644 |
0.01 |
1722.939698 |
342865 |
5.714416667 |
0.991489362 |
| 0.974083268 |
0.025916326 |
1712.040201 |
0.968157573 |
0.001 |
2158.79397 |
429600 |
7.16 |
0.992907801 |
| 0.923964157 |
0.07603503 |
1735.075377 |
0.968585243 |
9.00E-04 |
2138.497487 |
425561 |
7.092683333 |
0.992907801 |
| 0.753411266 |
0.246588817 |
1817.145729 |
0.972377253 |
8.00E-04 |
2181.904523 |
434199 |
7.23665 |
0.992907801 |
| 0.532621804 |
0.467378406 |
1967.728643 |
0.978036756 |
7.00E-04 |
2266.427136 |
451019 |
7.516983333 |
0.992907801 |
| 0.266563765 |
0.733435398 |
2466.407035 |
0.985468715 |
6.00E-04 |
2550.909548 |
507632 |
8.460533333 |
0.993380615 |
| 0.025442755 |
0.974557558 |
2922.668342 |
0.991769723 |
5.00E-04 |
2789.276382 |
555066 |
9.2511 |
0.992907801 |
| 3.34E-04 |
0.999666356 |
2991.839196 |
0.992444492 |
4.00E-04 |
2738.61809 |
544985 |
9.083083333 |
0.993380615 |
| 2.61E-04 |
0.999738897 |
4905.969849 |
0.992969576 |
3.00E-04 |
3858.919598 |
767925 |
12.79875 |
0.994799054 |
| 1.13E-04 |
0.999886811 |
5184 |
0.994399895 |
2.00E-04 |
3963.472362 |
788748 |
13.1458 |
0.994799054 |
| 8.53E-05 |
0.99991484 |
5632.281407 |
0.993960346 |
1.00E-04 |
4280.080402 |
851737 |
14.19561667 |
0.994799054 |
| 6.87E-05 |
0.999931338 |
5723.949749 |
0.993421006 |
9.00E-05 |
4231.547739 |
842078 |
14.03463333 |
0.994799054 |
| 6.72E-05 |
0.999932802 |
5754.090452 |
0.993409126 |
8.00E-05 |
4301.301508 |
855959 |
14.26598333 |
0.994799054 |
| 5.81E-05 |
0.99994191 |
6029.919598 |
0.993663352 |
7.00E-05 |
4460.60804 |
887661 |
14.79435 |
0.994799054 |
| 4.07E-05 |
0.999959309 |
6686.894472 |
0.99426209 |
6.00E-05 |
4802.758794 |
955750 |
15.92916667 |
0.994799054 |
| 3.96E-05 |
0.999960391 |
6733.869347 |
0.994269218 |
5.00E-05 |
4818.361809 |
958890 |
15.9815 |
0.994799054 |
| 2.97E-05 |
0.999970307 |
7164.261307 |
0.99394609 |
4.00E-05 |
5125.984925 |
1020072 |
17.0012 |
0.994799054 |
| 1.52E-05 |
0.999984755 |
7876 |
0.993435262 |
3.00E-05 |
5556.512563 |
1105747 |
18.42911667 |
0.993853428 |
| 1.50E-05 |
0.999984982 |
7898.773869 |
0.993437638 |
2.00E-05 |
5535.613065 |
1101588 |
18.3598 |
0.994326241 |
| 8.24E-06 |
0.999991749 |
10519.22613 |
0.994366632 |
1.00E-05 |
6211.005025 |
1235991 |
20.59985 |
0.995271868 |
| 7.83E-06 |
0.99999217 |
10562.28141 |
0.994266842 |
9.00E-06 |
7234.301508 |
1439627 |
23.99378333 |
0.994799054 |
| 6.59E-06 |
0.999993413 |
10718.19095 |
0.993960346 |
8.00E-06 |
7327.748744 |
1458222 |
24.3037 |
0.995271868 |
| 5.83E-06 |
0.999994177 |
10886.38191 |
0.993810661 |
7.00E-06 |
7197.854271 |
1432382 |
23.87303333 |
0.994799054 |
| 5.41E-06 |
0.999994592 |
11353.8392 |
0.994031624 |
6.00E-06 |
7603.512563 |
1513116 |
25.2186 |
0.994799054 |
| 4.47E-06 |
0.999995529 |
13691.06533 |
0.994314361 |
5.00E-06 |
8798.658291 |
1750966 |
29.18276667 |
0.994799054 |
| 3.26E-06 |
0.999996747 |
16101.14573 |
0.993972225 |
4.00E-06 |
9148.165829 |
1820485 |
30.34141667 |
0.994799054 |
| 2.31E-06 |
0.999997686 |
16729.59799 |
0.993385367 |
3.00E-06 |
10508.29648 |
2091169 |
34.85281667 |
0.993853428 |
| 1.14E-06 |
0.999998862 |
17837.20603 |
0.993836796 |
2.00E-06 |
9935.180905 |
1977133 |
32.95221667 |
0.993853428 |
| 8.60E-07 |
0.999999139 |
20745.63819 |
0.993896195 |
1.00E-06 |
12373.14573 |
2462271 |
41.03785 |
0.994326241 |
| 7.97E-07 |
0.999999204 |
21273.60804 |
0.993903323 |
9.00E-07 |
12732.64322 |
2533802 |
42.23003333 |
0.994799054 |
| 6.87E-07 |
0.999999313 |
22264.36181 |
0.994050631 |
8.00E-07 |
13595.05025 |
2705417 |
45.09028333 |
0.994799054 |
| 6.10E-07 |
0.99999939 |
23553.17588 |
0.993943714 |
7.00E-07 |
13737.59799 |
2733784 |
45.56306667 |
0.994799054 |
| 4.87E-07 |
0.999999513 |
26313.60804 |
0.993470901 |
6.00E-07 |
15162.24623 |
3017303 |
50.28838333 |
0.994799054 |
| 4.06E-07 |
0.999999593 |
27056.1407 |
0.993577818 |
5.00E-07 |
14951.58291 |
2975365 |
49.58941667 |
0.994799054 |
| 2.56E-07 |
0.999999744 |
27868 |
0.994055383 |
4.00E-07 |
15827.96482 |
3149765 |
52.49608333 |
0.994799054 |
| 2.54E-07 |
0.999999745 |
27998.54271 |
0.994026872 |
3.00E-07 |
15686.69347 |
3121656 |
52.0276 |
0.994799054 |
| 1.72E-07 |
0.999999827 |
34769.20603 |
0.99349466 |
2.00E-07 |
19260.24623 |
3832821 |
63.88035 |
0.995271868 |
| 8.67E-08 |
0.999999913 |
38491.84925 |
0.994183684 |
1.00E-07 |
21997.96985 |
4377613 |
72.96021667 |
0.994799054 |
| 81*30*49*30*2 |
||||||||
| f2[0] |
f2[1] |
迭代次数n |
平均准确率p-ave |
δ |
耗时ms/次 |
耗时ms/199次 |
耗时 min/199 |
最大值p-max |
| 0.497397008 |
0.501220403 |
17.31155779 |
0.502438908 |
0.5 |
1205.964824 |
240006 |
4.0001 |
0.810874704 |
| 0.58726634 |
0.412611563 |
3767.874372 |
0.906190527 |
0.4 |
5156.79397 |
1026202 |
17.10336667 |
0.997163121 |
| 0.636640566 |
0.36278485 |
3700.497487 |
0.99093339 |
0.3 |
5092.331658 |
1013374 |
16.88956667 |
0.99858156 |
| 0.654326877 |
0.345588026 |
3768.60804 |
0.996196111 |
0.2 |
6176.577889 |
1229139 |
20.48565 |
0.999054374 |
| 0.700519621 |
0.299452945 |
3784.150754 |
0.996065434 |
0.1 |
6700.40201 |
1333382 |
22.22303333 |
0.999054374 |
| 0.532134869 |
0.467869686 |
3937.753769 |
0.995502334 |
0.01 |
6800.080402 |
1353219 |
22.55365 |
0.999054374 |
| 0.422209109 |
0.577793465 |
4656.899497 |
0.989992516 |
0.001 |
7725.658291 |
1537412 |
25.62353333 |
1 |
| 0.341900165 |
0.65810115 |
4732.698492 |
0.991353933 |
9.00E-04 |
7944.38191 |
1580939 |
26.34898333 |
1 |
| 0.341879526 |
0.658120602 |
4773.040201 |
0.989754921 |
8.00E-04 |
8006.633166 |
1593322 |
26.55536667 |
0.999527187 |
| 0.296683534 |
0.703317013 |
4867.150754 |
0.990120817 |
7.00E-04 |
8641.472362 |
1719654 |
28.6609 |
1 |
| 0.186211679 |
0.813787865 |
5051.743719 |
0.99272723 |
6.00E-04 |
7573.653266 |
1507158 |
25.1193 |
0.999527187 |
| 0.201214859 |
0.798784971 |
5123.552764 |
0.991821994 |
5.00E-04 |
8622.60804 |
1715899 |
28.59831667 |
0.999527187 |
| 0.105760421 |
0.894239063 |
5354.537688 |
0.994896468 |
4.00E-04 |
8171.38191 |
1626105 |
27.10175 |
0.999527187 |
| 0.07558383 |
0.924415606 |
5771.562814 |
0.995005762 |
3.00E-04 |
9486.904523 |
1887894 |
31.4649 |
0.999527187 |
| 0.251341598 |
0.748658773 |
6608.065327 |
0.994136165 |
2.00E-04 |
10246.69849 |
2039093 |
33.98488333 |
0.998108747 |
| 0.964739483 |
0.035260641 |
9968.100503 |
0.991389572 |
1.00E-04 |
14835.39196 |
2952260 |
49.20433333 |
1 |
| 0.979821643 |
0.020178336 |
10986.21608 |
0.993760766 |
9.00E-05 |
16085.84925 |
3201101 |
53.35168333 |
0.999527187 |
| 0.994905063 |
0.005094859 |
11628.0201 |
0.994818062 |
8.00E-05 |
16933.49246 |
3369765 |
56.16275 |
0.999527187 |
| 0.999940459 |
5.95E-05 |
12679.52764 |
0.996609525 |
7.00E-05 |
18420.98995 |
3665777 |
61.09628333 |
0.999527187 |
| 0.999949295 |
5.05E-05 |
13109.78894 |
0.996367179 |
6.00E-05 |
19074.0201 |
3795730 |
63.26216667 |
0.999527187 |
| 0.999958601 |
4.14E-05 |
13711.12563 |
0.992064341 |
5.00E-05 |
19857.02513 |
3951548 |
65.85913333 |
0.999527187 |
| 0.999966141 |
3.39E-05 |
15135.42211 |
0.988716633 |
4.00E-05 |
21707.40201 |
4319773 |
71.99621667 |
0.999054374 |
| 0.999973419 |
2.65E-05 |
20739.68844 |
0.987889804 |
3.00E-05 |
29361.32663 |
5842921 |
97.38201667 |
0.997163121 |
| 0.999981476 |
1.85E-05 |
33675.49246 |
0.990051915 |
2.00E-05 |
40558.68342 |
8071202 |
134.5200333 |
0.996690307 |
| 0.999990802 |
9.20E-06 |
56278.17085 |
0.989160935 |
1.00E-05 |
75119.59799 |
14948816 |
249.1469333 |
0.994326241 |
| 0.999991844 |
8.16E-06 |
59442.55276 |
0.989065897 |
9.00E-06 |
79195.29648 |
15759865 |
262.6644167 |
0.992907801 |
| 0.999992753 |
7.24E-06 |
62130.47739 |
0.989201326 |
8.00E-06 |
83299.57789 |
16576632 |
276.2772 |
0.992434988 |
| 0.999993663 |
6.33E-06 |
66274.23618 |
0.989217957 |
7.00E-06 |
70169.71357 |
13963774 |
232.7295667 |
0.992907801 |
| 0.999994542 |
5.46E-06 |
70016.88442 |
0.989120544 |
6.00E-06 |
73501.49246 |
14626829 |
243.7804833 |
0.992434988 |
| 0.999995425 |
4.58E-06 |
75014.68844 |
0.988958979 |
5.00E-06 |
78995.73367 |
15720182 |
262.0030333 |
0.992907801 |
| 0.999996283 |
3.72E-06 |
81911.47739 |
0.988844934 |
4.00E-06 |
85623.82915 |
17039143 |
283.9857167 |
0.994799054 |
| 0.999997181 |
2.82E-06 |
92364.45729 |
0.989270228 |
3.00E-06 |
96463.50754 |
19196238 |
319.9373 |
0.994799054 |
| 0.999998086 |
1.91E-06 |
113501.8392 |
0.99128503 |
2.00E-06 |
118442.201 |
23570002 |
392.8333667 |
0.998108747 |
| 0.999999045 |
9.56E-07 |
154350.5126 |
0.991068819 |
1.00E-06 |
168604.7085 |
33552338 |
559.2056333 |
0.99858156 |
| con（3*3）-49-30-2 |
||||||||
| f2[0] |
f2[1] |
迭代次数n |
平均准确率p-ave |
δ |
耗时ms/次 |
耗时ms/199次 |
耗时 min/199 |
最大值p-max |
| 0.500276107 |
0.498344763 |
16.48241206 |
0.517770888 |
0.5 |
679.9798995 |
135316 |
2.255266667 |
0.867612293 |
| 0.5818015 |
0.418241698 |
1361.045226 |
0.75949725 |
0.4 |
1073.572864 |
213641 |
3.560683333 |
0.996690307 |
| 0.644166219 |
0.35596743 |
1720.190955 |
0.914275871 |
0.3 |
1165.959799 |
232026 |
3.8671 |
0.998108747 |
| 0.725259837 |
0.274933998 |
1943.336683 |
0.944265061 |
0.2 |
1230.040201 |
244795 |
4.079916667 |
0.998108747 |
| 0.814017223 |
0.186277612 |
2060.557789 |
0.953538377 |
0.1 |
1265.929648 |
251920 |
4.198666667 |
0.997635934 |
| 0.873548828 |
0.126458355 |
2972.638191 |
0.928536298 |
0.01 |
1535.18593 |
305502 |
5.0917 |
0.998108747 |
| 0.873795911 |
0.12620395 |
4091.859296 |
0.907932095 |
0.001 |
1856.603015 |
369480 |
6.158 |
0.99858156 |
| 0.868847942 |
0.131153585 |
4177.100503 |
0.890459389 |
9.00E-04 |
1886.482412 |
375411 |
6.25685 |
0.99858156 |
| 0.883973522 |
0.116025939 |
4204.231156 |
0.901728501 |
8.00E-04 |
1892.964824 |
376700 |
6.278333333 |
0.998108747 |
| 0.858927553 |
0.141072196 |
4327.341709 |
0.897024128 |
7.00E-04 |
1926.211055 |
383316 |
6.3886 |
0.997635934 |
| 0.879064535 |
0.120935924 |
4319.231156 |
0.898440191 |
6.00E-04 |
1909.361809 |
379963 |
6.332716667 |
0.999527187 |
| 0.843952852 |
0.156046265 |
4640.180905 |
0.881307245 |
5.00E-04 |
2000.708543 |
398141 |
6.635683333 |
0.998108747 |
| 0.823934671 |
0.17606553 |
4781.437186 |
0.872397448 |
4.00E-04 |
2072.291457 |
412387 |
6.873116667 |
0.998108747 |
| 0.879229957 |
0.120769961 |
4958.276382 |
0.884989962 |
3.00E-04 |
2162.969849 |
430431 |
7.17385 |
0.999054374 |
| 0.819003562 |
0.180996744 |
5410.175879 |
0.887848225 |
2.00E-04 |
2282.919598 |
454317 |
7.57195 |
0.998108747 |
| 0.859247532 |
0.1407525 |
5985.060302 |
0.847616332 |
1.00E-04 |
2843.241206 |
565810 |
9.430166667 |
0.999054374 |
| 0.829105325 |
0.170894589 |
5960.879397 |
0.870898226 |
9.00E-05 |
2404.231156 |
478448 |
7.974133333 |
0.99858156 |
| 0.824083387 |
0.17591666 |
6234.261307 |
0.846815638 |
8.00E-05 |
2956.020101 |
588254 |
9.804233333 |
0.999527187 |
| 0.864285382 |
0.135714544 |
6227.025126 |
0.858384119 |
7.00E-05 |
2947.98995 |
586658 |
9.777633333 |
0.99858156 |
| 0.909514101 |
0.090486022 |
6410.015075 |
0.822433681 |
6.00E-05 |
3032.884422 |
603550 |
10.05916667 |
0.999054374 |
| 0.894444864 |
0.105555205 |
6843.527638 |
0.812162467 |
5.00E-05 |
3155.98995 |
628044 |
10.4674 |
0.99858156 |
| 0.874349802 |
0.125650215 |
7226.582915 |
0.833256115 |
4.00E-05 |
3278.140704 |
652354 |
10.87256667 |
0.999527187 |
| 0.949728548 |
0.050271421 |
7567.170854 |
0.860681659 |
3.00E-05 |
3384.301508 |
673479 |
11.22465 |
0.999054374 |
| 0.884410163 |
0.115589827 |
8543.718593 |
0.861140216 |
2.00E-05 |
3696.969849 |
735703 |
12.26171667 |
0.999054374 |
| 0.859291217 |
0.140708784 |
10002.80905 |
0.868161137 |
1.00E-05 |
4203.839196 |
836569 |
13.94281667 |
0.998108747 |
| 0.849241442 |
0.150758558 |
10314.70854 |
0.856100835 |
9.00E-06 |
4305.170854 |
856730 |
14.27883333 |
0.999054374 |
| 0.84421681 |
0.155783197 |
10521.40704 |
0.856908657 |
8.00E-06 |
4360.839196 |
867813 |
14.46355 |
0.999054374 |
| 0.85929264 |
0.140707354 |
10795.59296 |
0.851536643 |
7.00E-06 |
4624.854271 |
920350 |
15.33916667 |
0.999054374 |
| 0.864318388 |
0.135681609 |
11356.43216 |
0.835860152 |
6.00E-06 |
4864.507538 |
968042 |
16.13403333 |
0.999527187 |
| 0.864318891 |
0.135681109 |
11524.1407 |
0.839217363 |
5.00E-06 |
5046.773869 |
1004311 |
16.73851667 |
0.999527187 |
| 0.81406843 |
0.185931571 |
12755.88945 |
0.82416337 |
4.00E-06 |
5486.366834 |
1091790 |
18.1965 |
0.999054374 |
| 0.83416934 |
0.165830664 |
13319.76884 |
0.84103021 |
3.00E-06 |
5700.80402 |
1134465 |
18.90775 |
0.999054374 |
| 0.76884336 |
0.231156641 |
15362.49749 |
0.857692719 |
2.00E-06 |
6409.427136 |
1275478 |
21.25796667 |
0.999054374 |
| 0.788944292 |
0.211055707 |
17225.44221 |
0.887536976 |
1.00E-06 |
7071.768844 |
1407285 |
23.45475 |
0.999054374 |
| 0.798994592 |
0.201005409 |
18530.14573 |
0.856122219 |
9.00E-07 |
7652.743719 |
1522897 |
25.38161667 |
0.99858156 |
| 0.773869028 |
0.226130972 |
19642.81407 |
0.869472659 |
8.00E-07 |
7851.894472 |
1562527 |
26.04211667 |
0.999527187 |
| 0.773869071 |
0.226130929 |
19718.48241 |
0.872692066 |
7.00E-07 |
8259.356784 |
1643628 |
27.3938 |
0.999527187 |
| 0.798994697 |
0.201005303 |
20614.09548 |
0.873711346 |
6.00E-07 |
8532.537688 |
1697991 |
28.29985 |
0.999527187 |
| 0.743718397 |
0.256281603 |
22309.09548 |
0.861109329 |
5.00E-07 |
8456.236181 |
1682806 |
28.04676667 |
0.999527187 |
| 0.778894301 |
0.221105699 |
22176.38693 |
0.871563491 |
4.00E-07 |
9060.462312 |
1803032 |
30.05053333 |
1 |
| 0.74371848 |
0.25628152 |
25596.88442 |
0.872273899 |
3.00E-07 |
9779.678392 |
1946157 |
32.43595 |
0.999054374 |
| 0.733668267 |
0.266331734 |
31598.69347 |
0.874267318 |
2.00E-07 |
12587.65327 |
2504949 |
41.74915 |
0.999054374 |
| 0.753768805 |
0.246231195 |
37973.84925 |
0.896722383 |
1.00E-07 |
14562.95477 |
2898029 |
48.30048333 |
0.999527187 |
