标准BP神经网络算法程序

1、1标准BP神经网络算法程序：T=1:1:137;%输入样本和目标输出p= 0.32196 0.24591 0.16613 0. 0. -0.04695 -0.10237 -0.15076 -0.19321 -0.23058 -0.26353 -0.29257 -0.31803 -0.34018 -0.35918 -0.37519 -0.38828 -0.39853 -0.40598 -0.41068 -0.41266 -0.41198 -0.4087 -0.4029 -0.39469 -0.38422 -0.37166 -0.35725 -0.34126 -0.324 -0.30582 -0.

2、28709 -0.2682 -0.24951 -0.23138 -0.21408 -0.19785 -0.18282 -0.16908 -0.15662 -0.14542 -0.13539 -0.12643 -0.11842 -0.11125 -0.1048 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.04846 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.02936 -0. -0. -0.02555 -0. -0.02328 -0.02222 -0. -0. -0. -

3、0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.; -0.51781 -0.3674 -0.20883 -0. 0. 0.21728 0.32875 0.42639 0.51226 0.58808 0.65512

4、 0.7143 0.76629 0.81155 0.85042 0.88311 0.90979 0.93056 0.9455 0.95468 0.95817 0.95606 0.94848 0.93561 0.91766 0.89496 0.86789 0.83691 0.8026 0.76561 0.72666 0.68651 0.64597 0.6058 0.56669 0.52924 0.49392 0.46102 0.43071 0.40302 0.37787 0.35511 0.33453 0.31591 0.29903 0.28366 0.2696 0.25668 0.24474

5、0.23365 0.22329 0.21356 0.2044 0.19573 0.18751 0.17968 0.17221 0.16508 0.15826 0.15172 0.14546 0.13944 0.13367 0.12813 0.1228 0.11769 0.11278 0.10806 0.10352 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.05392 0. 0.04941 0. 0. 0. 0. 0.03977 0. 0. 0. 0. 0. 0.03212 0. 0. 0. 0.02718 0. 0. 0. 0.02311 0. 0

6、. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.; -2.9024 -3.152 -3.192 -3.0144 -2.7059 -2.3718 -2.0688 -1.8102 -1.5912 -1.4028 -1.2371 -1.0879 -0.95071 -0.82207 -0.69957 -0.58143 -0.46641 -0.35366 -0.24269 -0.13334 -0. 0. 0.1823 0.28105 0.37474 0.4619 0.54

7、088 0.60993 0.66728 0.71133 0.74082 0.75502 0.75396 0.73848 0.71031 0.67184 0.62597 0.57567 0.52376 0.47261 0.42401 0.37915 0.33871 0.3029 0.27164 0.24462 0.22143 0.20161 0.18468 0.1702 0.15777 0.14705 0.13773 0.12956 0.12234 0.11588 0.11006 0.10476 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.

8、 0. 0. 0. 0. 0. 0.03777 0. 0. 0. 0.03125 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.0512e-010 1.0026e-010 9.5616e-011 9.1193e-011 8.6971e-011 8.2946e-011 7.9107e-011 7.5441e-011 7.1954e-011 6.862e-011 6.5437e-011 6.2412e-011 5.9522e-011 5.6766e-011 5.414e-011 5.163e-011 4.92

9、39e-011 4.6964e-011 4.4791e-011 4.2712e-011 4.0734e-011 3.8847e-011 3.705e-011 3.5335e-011 3.3705e-011 3.2149e-011 3.0654e-011 2.9233e-011 2.7884e-011 2.6593e-011; 5.7211 6.2514 6.3564 6.0212 5.42 4.7642 4.1677 3.6575 3.2241 2.8499 2.5192 2.2198 1.9425 1.6808 1.43 1.1866 0.94836 0.71371 0.4819 0.252

10、82 0. -0.19468 -0.41042 -0.61812 -0.81514 -0.9984 -1.1645 -1.3097 -1.4307 -1.524 -1.5872 -1.619 -1.6193 -1.59 -1.5346 -1.458 -1.366 -1.2647 -1.1598 -1.056 -0.95706 -0.86541 -0.7824 -0.70853 -0.64366 -0.58721 -0.53837 -0.49624 -0.4599 -0.42846 -0.40115 -0.37727 -0.35623 -0.33754 -0.32077 -0.30558 -0.

11、29171 -0.27893 -0.26707 -0.25596 -0.24552 -0.23564 -0.22625 -0.21729 -0.20872 -0.20051 -0.19261 -0.18502 -0.1777 -0.17065 -0.16385 -0.15728 -0.15094 -0.14482 -0.13891 -0.13321 -0.1277 -0.12239 -0.11726 -0.11232 -0.10755 -0.10296 -0. -0. -0. -0. -0.08241 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.05427 -

12、0. -0. -0. -0. -0.04267 -0. -0.0387 -0. -0. -0. -5.5315e-010 -5.2754e-010 -5.0313e-010 -4.7985e-010 -4.5763e-010 -4.3646e-010 -4.1626e-010 -3.9698e-010 -3.7862e-010 -3.6109e-010 -3.4436e-010 -3.2843e-010 -3.1323e-010 -2.9873e-010 -2.8491e-010 -2.7171e-010 -2.5913e-010 -2.4715e-010 -2.3571e-010 -2.24

13、79e-010 -2.1439e-010 -2.0446e-010 -1.95e-010 -1.8597e-010 -1.7738e-010 -1.6918e-010 -1.6133e-010 -1.5386e-010 -1.4675e-010 -1.3996e-010;t= 5.7406 1.5872 -1.5819 -3.3978 -4.1771 -4.5036 -4.75 -5.0459 -5.4062 -5.8125 -6.2426 -6.678 -7.1043 -7.5095 -7.8834 -8.2164 -8.4991 -8.7221 -8.8761 -8.9522 -8.942

14、7 -8.8412 -8.6438 -8.3497 -7.9621 -7.4882 -6.9402 -6.3349 -5.693 -5.0382 -4.3953 -3.7881 -3.2373 -2.7579 -2.3583 -2.0396 -1.7966 -1.6195 -1.4958 -1.4128 -1.3584 -1.3226 -1.2977 -1.278 -1.2598 -1.2406 -1.2194 -1.1955 -1.169 -1.1401 -1.1092 -1.0767 -1.0431 -1.0088 -0.9741 -0.93938 -0.90487 -0.87078 -0

15、.83727 -0.80447 -0.77246 -0.74132 -0.7111 -0.68183 -0.65351 -0.62617 -0.5998 -0.57439 -0.54992 -0.52639 -0.50378 -0.48205 -0.4612 -0.44119 -0.42201 -0.40362 -0.38601 -0.36914 -0.353 -0.33755 -0.32278 -0.30866 -0.29516 -0.28227 -0.26996 -0.25821 -0.24699 -0.23629 -0.22608 -0.21635 -0.20707 -0.19824 -

16、0.18982 -0.1818 -0.17416 -0.17416 -0.16689 -0.15998 -0.15339 -0.14713 -0.14118 -0.13552 -0.13014 -0.12502 -0.12016 -0.11554 -0.11115 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.;s1=4;s2=4;s3=1;%输入层，隐层以及输出层各层的神经元数R,Q=size(p);S

17、,Q=size(t);w1=-13.2406 -8.8883 1.2111 -2.1919;0.4954 0.20729 0.21402 0.10116;-0.34757 -0.13691 -0.15081 -0.07379;-0.39188 -0.15828 -0.1692 -0.;%输入层到隐层的权值b1=ones(4,137);%给输入层到隐层的阈值赋全一w2,b2=rands(s3,s2);b2=ones(1,137);%给隐层到输出层的阈值赋全一n1=w1*p+b1;a1=tansig(n1);%隐层的输出n2=w2*a1+b2;a2=purelin(n2);%输出层的输出net =

18、 newff(minmax(p),4,1,tansig purelin,traingd);% 创建一个新的前向神经网络% 设置训练参数net.trainParam.lr= 0.15;net.trainParam.show= 25;net.trainParam.epochs=17000;net.trainParam.goal=0.01;% 对 BP 网络进行仿真Y=sim(net,p);% 调用 TRAINGD 算法训练 BP 网络net = train(net,p,t);% 计算仿真误差E = t- Y;MSE=mse(E)plot(T,t,r+);grid on;hold onplot(T,

19、Y);grid on;hold off;pause clc p1=p(1,:);p2=p(2,:);p3=p(3,:);p4=p(4,:);plot(T,p1);grid on;pause clcplot(T,p2);grid on;pauseclcplot(T,p3);grid on;pauseclcplot(T,p4);grid on; pauseclcplot(T,E);2动量BP算法程序：T=1:1:137; %输入样本和目标输出p= 0.32196 0.24591 0.16613 0. 0. -0.04695 -0.10237 -0.15076 -0.19321 -0.23058 -

20、0.26353 -0.29257 -0.31803 -0.34018 -0.35918 -0.37519 -0.38828 -0.39853 -0.40598 -0.41068 -0.41266 -0.41198 -0.4087 -0.4029 -0.39469 -0.38422 -0.37166 -0.35725 -0.34126 -0.324 -0.30582 -0.28709 -0.2682 -0.24951 -0.23138 -0.21408 -0.19785 -0.18282 -0.16908 -0.15662 -0.14542 -0.13539 -0.12643 -0.11842

21、-0.11125 -0.1048 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.04846 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.02936 -0. -0. -0.02555 -0. -0.02328 -0.02222 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0

22、. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.; -0.51781 -0.3674 -0.20883 -0. 0. 0.21728 0.32875 0.42639 0.51226 0.58808 0.65512 0.7143 0.76629 0.81155 0.85042 0.88311 0.90979 0.93056 0.9455 0.95468 0.95817 0.95606 0.94848 0.93561 0.91766 0.

23、89496 0.86789 0.83691 0.8026 0.76561 0.72666 0.68651 0.64597 0.6058 0.56669 0.52924 0.49392 0.46102 0.43071 0.40302 0.37787 0.35511 0.33453 0.31591 0.29903 0.28366 0.2696 0.25668 0.24474 0.23365 0.22329 0.21356 0.2044 0.19573 0.18751 0.17968 0.17221 0.16508 0.15826 0.15172 0.14546 0.13944 0.13367 0.

24、12813 0.1228 0.11769 0.11278 0.10806 0.10352 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.05392 0. 0.04941 0. 0. 0. 0. 0.03977 0. 0. 0. 0. 0. 0.03212 0. 0. 0. 0.02718 0. 0. 0. 0.02311 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.; -2.9024 -3.152 -3.19

25、2 -3.0144 -2.7059 -2.3718 -2.0688 -1.8102 -1.5912 -1.4028 -1.2371 -1.0879 -0.95071 -0.82207 -0.69957 -0.58143 -0.46641 -0.35366 -0.24269 -0.13334 -0. 0. 0.1823 0.28105 0.37474 0.4619 0.54088 0.60993 0.66728 0.71133 0.74082 0.75502 0.75396 0.73848 0.71031 0.67184 0.62597 0.57567 0.52376 0.47261 0.424

26、01 0.37915 0.33871 0.3029 0.27164 0.24462 0.22143 0.20161 0.18468 0.1702 0.15777 0.14705 0.13773 0.12956 0.12234 0.11588 0.11006 0.10476 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.03777 0. 0. 0. 0.03125 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.0512

27、e-010 1.0026e-010 9.5616e-011 9.1193e-011 8.6971e-011 8.2946e-011 7.9107e-011 7.5441e-011 7.1954e-011 6.862e-011 6.5437e-011 6.2412e-011 5.9522e-011 5.6766e-011 5.414e-011 5.163e-011 4.9239e-011 4.6964e-011 4.4791e-011 4.2712e-011 4.0734e-011 3.8847e-011 3.705e-011 3.5335e-011 3.3705e-011 3.2149e-01

28、1 3.0654e-011 2.9233e-011 2.7884e-011 2.6593e-011; 5.7211 6.2514 6.3564 6.0212 5.42 4.7642 4.1677 3.6575 3.2241 2.8499 2.5192 2.2198 1.9425 1.6808 1.43 1.1866 0.94836 0.71371 0.4819 0.25282 0. -0.19468 -0.41042 -0.61812 -0.81514 -0.9984 -1.1645 -1.3097 -1.4307 -1.524 -1.5872 -1.619 -1.6193 -1.59 -1.

29、5346 -1.458 -1.366 -1.2647 -1.1598 -1.056 -0.95706 -0.86541 -0.7824 -0.70853 -0.64366 -0.58721 -0.53837 -0.49624 -0.4599 -0.42846 -0.40115 -0.37727 -0.35623 -0.33754 -0.32077 -0.30558 -0.29171 -0.27893 -0.26707 -0.25596 -0.24552 -0.23564 -0.22625 -0.21729 -0.20872 -0.20051 -0.19261 -0.18502 -0.1777

30、-0.17065 -0.16385 -0.15728 -0.15094 -0.14482 -0.13891 -0.13321 -0.1277 -0.12239 -0.11726 -0.11232 -0.10755 -0.10296 -0. -0. -0. -0. -0.08241 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.05427 -0. -0. -0. -0. -0.04267 -0. -0.0387 -0. -0. -0. -5.5315e-010 -5.2754e-010 -5.0313e-010 -4.7985e-010 -4.5763e-010

31、-4.3646e-010 -4.1626e-010 -3.9698e-010 -3.7862e-010 -3.6109e-010 -3.4436e-010 -3.2843e-010 -3.1323e-010 -2.9873e-010 -2.8491e-010 -2.7171e-010 -2.5913e-010 -2.4715e-010 -2.3571e-010 -2.2479e-010 -2.1439e-010 -2.0446e-010 -1.95e-010 -1.8597e-010 -1.7738e-010 -1.6918e-010 -1.6133e-010 -1.5386e-010 -1.

32、4675e-010 -1.3996e-010;t= 5.7406 1.5872 -1.5819 -3.3978 -4.1771 -4.5036 -4.75 -5.0459 -5.4062 -5.8125 -6.2426 -6.678 -7.1043 -7.5095 -7.8834 -8.2164 -8.4991 -8.7221 -8.8761 -8.9522 -8.9427 -8.8412 -8.6438 -8.3497 -7.9621 -7.4882 -6.9402 -6.3349 -5.693 -5.0382 -4.3953 -3.7881 -3.2373 -2.7579 -2.3583

33、-2.0396 -1.7966 -1.6195 -1.4958 -1.4128 -1.3584 -1.3226 -1.2977 -1.278 -1.2598 -1.2406 -1.2194 -1.1955 -1.169 -1.1401 -1.1092 -1.0767 -1.0431 -1.0088 -0.9741 -0.93938 -0.90487 -0.87078 -0.83727 -0.80447 -0.77246 -0.74132 -0.7111 -0.68183 -0.65351 -0.62617 -0.5998 -0.57439 -0.54992 -0.52639 -0.50378

34、-0.48205 -0.4612 -0.44119 -0.42201 -0.40362 -0.38601 -0.36914 -0.353 -0.33755 -0.32278 -0.30866 -0.29516 -0.28227 -0.26996 -0.25821 -0.24699 -0.23629 -0.22608 -0.21635 -0.20707 -0.19824 -0.18982 -0.1818 -0.17416 -0.17416 -0.16689 -0.15998 -0.15339 -0.14713 -0.14118 -0.13552 -0.13014 -0.12502 -0.1201

35、6 -0.11554 -0.11115 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.;s1=4;s2=4;s3=1;%输入层，隐层以及输出层各层的神经元数R,Q=size(p);S,Q=size(t);5w1=-13.2406 -8.8883 1.2111 -2.1919;0.4954 0.20729 0.21402 0.10116;-0.34757 -0.13691 -0.15081 -0.07379

36、;-0.39188 -0.15828 -0.1692 -0.;%输入层到隐层的权值b1=ones(4,137);%给输入层到隐层的阈值赋全一w2,b2=rands(s3,s2);b2=ones(1,137);%给隐层到输出层的阈值赋全一n1=w1*p+b1;a1=tansig(n1);%隐层的输出n2=w2*a1+b2;a2=purelin(n2);%输出层的输出 net = newff(minmax(p),4,1,tansig purelin,traingdm);% 创建一个新的前向神经网络% 设置训练参数net.trainParam.show= 25; net.trainParam.epo

37、chs=17000;net.trainParam.goal=0.001;lr=0.1;% 调用 TRAINGDM 算法训练 BP 网络net = train(net,p,t); % 对 BP 网络进行仿真Y=sim(net,p);% 计算仿真误差E = t- Y;MSE=mse(E)plot(T,t,r+);grid on;hold onplot(T,Y);grid on;hold off;pause clc p1=p(1,:);p2=p(2,:);p3=p(3,:);p4=p(4,:); plot(T,p1);grid on;pauseclcplot(T,p2);grid on;pausec

38、lcplot(T,p3);grid on;pauseclcplot(T,p4);grid on; pauseclcplot(T,E);3学习率可变的动量BP算法T=1:1:137;p= 0.32196 0.24591 0.16613 0. 0. -0.04695 -0.10237 -0.15076 -0.19321 -0.23058 -0.26353 -0.29257 -0.31803 -0.34018 -0.35918 -0.37519 -0.38828 -0.39853 -0.40598 -0.41068 -0.41266 -0.41198 -0.4087 -0.4029 -0.39469

39、 -0.38422 -0.37166 -0.35725 -0.34126 -0.324 -0.30582 -0.28709 -0.2682 -0.24951 -0.23138 -0.21408 -0.19785 -0.18282 -0.16908 -0.15662 -0.14542 -0.13539 -0.12643 -0.11842 -0.11125 -0.1048 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.04846 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.02

40、936 -0. -0. -0.02555 -0. -0.02328 -0.02222 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.; -0.51781 -0.3674 -0.2088

41、3 -0. 0. 0.21728 0.32875 0.42639 0.51226 0.58808 0.65512 0.7143 0.76629 0.81155 0.85042 0.88311 0.90979 0.93056 0.9455 0.95468 0.95817 0.95606 0.94848 0.93561 0.91766 0.89496 0.86789 0.83691 0.8026 0.76561 0.72666 0.68651 0.64597 0.6058 0.56669 0.52924 0.49392 0.46102 0.43071 0.40302 0.37787 0.35511

42、 0.33453 0.31591 0.29903 0.28366 0.2696 0.25668 0.24474 0.23365 0.22329 0.21356 0.2044 0.19573 0.18751 0.17968 0.17221 0.16508 0.15826 0.15172 0.14546 0.13944 0.13367 0.12813 0.1228 0.11769 0.11278 0.10806 0.10352 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.05392 0. 0.04941 0. 0. 0. 0. 0.03977 0. 0.

43、 0. 0. 0. 0.03212 0. 0. 0. 0.02718 0. 0. 0. 0.02311 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.; -2.9024 -3.152 -3.192 -3.0144 -2.7059 -2.3718 -2.0688 -1.8102 -1.5912 -1.4028 -1.2371 -1.0879 -0.95071 -0.82207 -0.69957 -0.58143 -0.46641 -0.35366 -0.

44、24269 -0.13334 -0. 0. 0.1823 0.28105 0.37474 0.4619 0.54088 0.60993 0.66728 0.71133 0.74082 0.75502 0.75396 0.73848 0.71031 0.67184 0.62597 0.57567 0.52376 0.47261 0.42401 0.37915 0.33871 0.3029 0.27164 0.24462 0.22143 0.20161 0.18468 0.1702 0.15777 0.14705 0.13773 0.12956 0.12234 0.11588 0.11006 0.

45、10476 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.03777 0. 0. 0. 0.03125 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.0512e-010 1.0026e-010 9.5616e-011 9.1193e-011 8.6971e-011 8.2946e-011 7.9107e-011 7.5441e-011 7.1954e-011 6.862e-011 6.5437e-011 6.2412

46、e-011 5.9522e-011 5.6766e-011 5.414e-011 5.163e-011 4.9239e-011 4.6964e-011 4.4791e-011 4.2712e-011 4.0734e-011 3.8847e-011 3.705e-011 3.5335e-011 3.3705e-011 3.2149e-011 3.0654e-011 2.9233e-011 2.7884e-011 2.6593e-011; 5.7211 6.2514 6.3564 6.0212 5.42 4.7642 4.1677 3.6575 3.2241 2.8499 2.5192 2.219

47、8 1.9425 1.6808 1.43 1.1866 0.94836 0.71371 0.4819 0.25282 0. -0.19468 -0.41042 -0.61812 -0.81514 -0.9984 -1.1645 -1.3097 -1.4307 -1.524 -1.5872 -1.619 -1.6193 -1.59 -1.5346 -1.458 -1.366 -1.2647 -1.1598 -1.056 -0.95706 -0.86541 -0.7824 -0.70853 -0.64366 -0.58721 -0.53837 -0.49624 -0.4599 -0.42846 -

48、0.40115 -0.37727 -0.35623 -0.33754 -0.32077 -0.30558 -0.29171 -0.27893 -0.26707 -0.25596 -0.24552 -0.23564 -0.22625 -0.21729 -0.20872 -0.20051 -0.19261 -0.18502 -0.1777 -0.17065 -0.16385 -0.15728 -0.15094 -0.14482 -0.13891 -0.13321 -0.1277 -0.12239 -0.11726 -0.11232 -0.10755 -0.10296 -0. -0. -0. -0.

49、 -0.08241 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.05427 -0. -0. -0. -0. -0.04267 -0. -0.0387 -0. -0. -0. -5.5315e-010 -5.2754e-010 -5.0313e-010 -4.7985e-010 -4.5763e-010 -4.3646e-010 -4.1626e-010 -3.9698e-010 -3.7862e-010 -3.6109e-010 -3.4436e-010 -3.2843e-010 -3.1323e-010 -2.9873e-010 -2.8491e-010 -

50、2.7171e-010 -2.5913e-010 -2.4715e-010 -2.3571e-010 -2.2479e-010 -2.1439e-010 -2.0446e-010 -1.95e-010 -1.8597e-010 -1.7738e-010 -1.6918e-010 -1.6133e-010 -1.5386e-010 -1.4675e-010 -1.3996e-010;t= 5.7406 1.5872 -1.5819 -3.3978 -4.1771 -4.5036 -4.75 -5.0459 -5.4062 -5.8125 -6.2426 -6.678 -7.1043 -7.509

51、5 -7.8834 -8.2164 -8.4991 -8.7221 -8.8761 -8.9522 -8.9427 -8.8412 -8.6438 -8.3497 -7.9621 -7.4882 -6.9402 -6.3349 -5.693 -5.0382 -4.3953 -3.7881 -3.2373 -2.7579 -2.3583 -2.0396 -1.7966 -1.6195 -1.4958 -1.4128 -1.3584 -1.3226 -1.2977 -1.278 -1.2598 -1.2406 -1.2194 -1.1955 -1.169 -1.1401 -1.1092 -1.07

52、67 -1.0431 -1.0088 -0.9741 -0.93938 -0.90487 -0.87078 -0.83727 -0.80447 -0.77246 -0.74132 -0.7111 -0.68183 -0.65351 -0.62617 -0.5998 -0.57439 -0.54992 -0.52639 -0.50378 -0.48205 -0.4612 -0.44119 -0.42201 -0.40362 -0.38601 -0.36914 -0.353 -0.33755 -0.32278 -0.30866 -0.29516 -0.28227 -0.26996 -0.25821

53、 -0.24699 -0.23629 -0.22608 -0.21635 -0.20707 -0.19824 -0.18982 -0.1818 -0.17416 -0.17416 -0.16689 -0.15998 -0.15339 -0.14713 -0.14118 -0.13552 -0.13014 -0.12502 -0.12016 -0.11554 -0.11115 -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0. -0.

54、 -0. -0.;s1=4;s2=4;s3=1;R,Q=size(p);S,Q=size(t);5w1=-13.2406 -8.8883 1.2111 -2.1919;0.4954 0.20729 0.21402 0.10116;-0.34757 -0.13691 -0.15081 -0.07379;-0.39188 -0.15828 -0.1692 -0.;b1=ones(4,137);%给输入层到隐层的阈值赋全一w2,b2=rands(s3,s2);b2=ones(1,137);%给隐层到输出层的阈值赋全一n1=w1*p+b1;a1=tansig(n1);%隐层的输出n2=w2*a1+b2

55、;a2=purelin(n2);%输出层的输出 net = newff(minmax(p),4,1,tansig purelin,traingdx);% 创建一个新的前向神经网络% 设置训练参数net.trainParam.show= 25; net.trainParam.epochs=17000;net.trainParam.goal=0.001;lr=0.1;% 调用 TRAINGDX 算法训练 BP 网络net = train(net,p,t); % 对 BP 网络进行仿真Y=sim(net,p);% 计算仿真误差E = t- Y;MSE=mse(E)plot(T,t,r+);grid

56、on;hold onplot(T,Y);grid on;hold off;pause clc p1=p(1,:);p2=p(2,:);p3=p(3,:);p4=p(4,:); plot(T,p1);grid on;pauseclcplot(T,p2);grid on;pauseclcplot(T,p3);grid on;pauseclcplot(T,p4);grid on; pauseclcplot(T,E);4粒子群优化神经网络源程序k=-0.7412-648.09677.931184.1;global k;w2=k;a=0.729;%惯性权值b1=1.494;%加速系数b2=1.494;f

57、itness=5.7;Number_Particle = 30;%粒子群个数Dimension =4;%维数Generation = 10;%代数Max_Velocity = 10;%最大速度Max_Swarm = 20;%最大位置Disturb = 5;%初始化Swarm = Max_Swarm*(rand(Number_Particle, Dimension)-0.5);Velocity = Max_Velocity*(rand(Number_Particle, Dimension)-0.5);Local_Best_Particle = Swarm;k = Swarm(1,:);Global_Best_Particle = k;Global_Best_Fitness =fitness;%输入样本进行网络训练T=1:1:137;p= 0.32196 0.24591 0.16613 0. 0. -0.04695 -0.10237 -0.15076 -0.19321 -0.23058 -0.26353 -0.29257 -0.31803 -0.34018 -0.35918 -0.37519 -0.38828 -0.39853 -0.40598 -0.41068 -0.4