-
Notifications
You must be signed in to change notification settings - Fork 694
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
d66153d
commit 9642df1
Showing
112 changed files
with
1,165 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,18 @@ | ||
| Volume in drive G is 新加卷 | ||
| Volume Serial Number is FEFD-7BE2 | ||
|
|
||
| Directory of G:\数学建模\3算法\现代的算法 | ||
|
|
||
| 2022/06/13 22:44 <DIR> . | ||
| 2022/06/13 22:44 <DIR> .. | ||
| 2022/06/13 22:44 0 1.txt | ||
| 2022/03/25 19:08 <DIR> 小波分析 | ||
| 2022/03/25 19:08 <DIR> 模拟退火法 | ||
| 2022/04/04 00:22 <DIR> 神经网络 | ||
| 2022/03/25 19:08 <DIR> 粒子群算法 | ||
| 2022/03/25 19:08 <DIR> 蒙特卡洛法 | ||
| 2022/03/25 19:08 <DIR> 计算机仿真 | ||
| 2022/03/25 19:08 <DIR> 遗传算法 | ||
| 2022/03/25 19:08 <DIR> 马尔科夫链 | ||
| 1 File(s) 0 bytes | ||
| 10 Dir(s) 681,250,013,184 bytes free |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| function main() | ||
| clc;clear all;close all; | ||
| %用Mexihat函数作为样本输入和输出 | ||
| x=0:0.03:3; %样本输入值 | ||
| c=2/(sqrt(3).*pi.^(1/4)); | ||
| d=1/sqrt(2); | ||
| u=x/2-1; | ||
| targ=d.*c.*exp(-u.^2/2).*(1-u.^2); % 目标函数的样本输出值 | ||
| eta=0.02;aerfa=0.735; %赋予网络学习速率和动量因子初始值 | ||
| %初始化输出层和隐层的连接权wjh和隐层和输出层的连接权. | ||
| %假设小波函数节点数为H,样本数为P,输出节点数为J,输入节点数为I. | ||
| H=15;P=2;I=length(x);J=length(targ); | ||
| b=rand(H,1);a=rand(H,1); %初始化小波参数 | ||
| whi=rand(I,H);wjh=rand(H,J); %初始化权系数； | ||
| b1=rand(H,1);b2=rand(J,1);%阈值初始化; | ||
| p=0; | ||
| Err_NetOut=[];%保存的误差； | ||
| flag=1;count=0; | ||
| while flag>0 | ||
| flag=0; | ||
| count=count+1; | ||
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | ||
| xhp1=0; | ||
| for h=1:H | ||
| for i=1:I | ||
| xhp1=xhp1+whi(i,h)*x(i); | ||
| end | ||
| ixhp(h)=xhp1+b1(h); | ||
| xhp1=0; | ||
| end | ||
| for h=1:H | ||
| oxhp(h)=fai((ixhp(h)-b(h))/a(h)); | ||
| end | ||
| ixjp1=0; | ||
| for j=1:J | ||
| for h=1:H | ||
| ixjp1=ixjp1+wjh(h,j)*oxhp(h); | ||
| end | ||
| ixjp(j)=ixjp1+b2(j); | ||
| ixjp1=0; | ||
| end | ||
| for i=1:J | ||
| oxjp(i)=fnn(ixjp(i)); | ||
| end | ||
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | ||
| wuchayy=1/2*sumsqr(oxjp-targ); | ||
| Err_NetOut=[Err_NetOut wuchayy];%保存每次的误差; | ||
| %求解小波网络运用BP算法，各参数每次学习的调整量 | ||
| for j=1:J | ||
| detaj(j)=-(oxjp(j)-targ(j))*oxjp(j)*(1-oxjp(j)); | ||
| end | ||
| for j=1:J | ||
| for h=1:H | ||
| detawjh(h,j)=eta*detaj(j)*oxhp(h); | ||
| end | ||
| end | ||
| detab2=eta*detaj; | ||
| sum=0; | ||
| for h=1:H | ||
| for j=1:J | ||
| sum=detaj(j)*wjh(h,j)*diffai((ixhp(h)-b(h))/a(h))/a(h)+sum; | ||
| end | ||
| detah(h)=sum; | ||
| sum=0; | ||
| end | ||
| for h=1:H | ||
| for i=1:I | ||
| detawhi(i,h)=eta*detah(h)*x(i); | ||
| end | ||
| end | ||
| detab1=eta*detah; | ||
| detab=-eta*detah; | ||
| for h=1:H | ||
| detaa(h)=-eta*detah(h)*((ixhp(h)-b(h))/a(h)); | ||
| end | ||
| %引入动量因子aerfa，加快收敛速度和阻碍陷入局部极小值. | ||
| wjh=wjh+(1+aerfa)*detawjh; | ||
| whi=whi+(1+aerfa)*detawhi; | ||
| a=a+(1+aerfa)*detaa'; | ||
| b=b+(1+aerfa)*detab'; | ||
| b1=b1+(1+aerfa)*detab1'; | ||
| b2=b2+(1+aerfa)*detab2'; | ||
| %本算法采用的是样本逐个处理而不是数据批处理 | ||
| p=p+1; | ||
| if p~=P | ||
| flag=flag+1; | ||
| else | ||
| if Err_NetOut(end)>0.008 | ||
| flag=flag+1; | ||
| else | ||
| figure; | ||
| plot(Err_NetOut); | ||
| xlabel('网络学习的次数');ylabel('网络输出的误差'); | ||
| title('网络学习误差曲线','fontsize',20,'color',[0 1 1],'fontname','隶书'); | ||
| end | ||
| end | ||
| if count>6000 | ||
| figure(1); | ||
| subplot(1,2,1) | ||
| plot(Err_NetOut,'color','b','linestyle','-','linewidth',2.2,... | ||
| 'marker','^','markersize',3.5); | ||
| xlabel('网络学习的次数');ylabel('网络输出的误差'); | ||
| title('误差曲线','fontsize',20,'color',[1 1 1],'fontname','隶书'); | ||
| subplot(1,2,2) | ||
| handle1=plot(x,targ,'color','r','linestyle','--','linewidth',2.2,... | ||
| 'marker','p','markersize',3.5); | ||
| hold on | ||
| handle1=plot(x,oxjp,'color','g','linestyle','-.','linewidth',2.2,... | ||
| 'marker','d','markersize',3.5); | ||
| xlabel('样本输入值');ylabel('样本目标值与网络输出值'); | ||
| title('目标值与网络输出值比较','fontsize',20,'color',[1 1 1],'fontname','隶书'); | ||
| legend('样本目标值','网络仿真值'); | ||
| break; | ||
| end | ||
| end | ||
| function y3=diffai(x) %子程序 | ||
| y3=-1.75*sin(1.75*x).*exp(-x.^2/2)-cos(1.75*x).*exp(-x.^2/2).*x; | ||
| function yl=fai(x) %子程序 | ||
| yl=cos(1.75.*x).*exp(-x.^2/2); | ||
| function y2=fnn(x) %子程序 | ||
| y2=1/(1+exp(-x)); |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,127 @@ | ||
| clear | ||
| clc | ||
| a = 0.99; % �¶�˥�������IJ��� | ||
| t0 = 97; tf = 3; t = t0; | ||
| Markov_length = 10000; % Markov������ | ||
| coordinates = [ | ||
| 1 565.0 575.0; 2 25.0 185.0; 3 345.0 750.0; | ||
| 4 945.0 685.0; 5 845.0 655.0; 6 880.0 660.0; | ||
| 7 25.0 230.0; 8 525.0 1000.0; 9 580.0 1175.0; | ||
| 10 650.0 1130.0; 11 1605.0 620.0; 12 1220.0 580.0; | ||
| 13 1465.0 200.0; 14 1530.0 5.0; 15 845.0 680.0; | ||
| 16 725.0 370.0; 17 145.0 665.0; 18 415.0 635.0; | ||
| 19 510.0 875.0; 20 560.0 365.0; 21 300.0 465.0; | ||
| 22 520.0 585.0; 23 480.0 415.0; 24 835.0 625.0; | ||
| 25 975.0 580.0; 26 1215.0 245.0; 27 1320.0 315.0; | ||
| 28 1250.0 400.0; 29 660.0 180.0; 30 410.0 250.0; | ||
| 31 420.0 555.0; 32 575.0 665.0; 33 1150.0 1160.0; | ||
| 34 700.0 580.0; 35 685.0 595.0; 36 685.0 610.0; | ||
| 37 770.0 610.0; 38 795.0 645.0; 39 720.0 635.0; | ||
| 40 760.0 650.0; 41 475.0 960.0; 42 95.0 260.0; | ||
| 43 875.0 920.0; 44 700.0 500.0; 45 555.0 815.0; | ||
| 46 830.0 485.0; 47 1170.0 65.0; 48 830.0 610.0; | ||
| 49 605.0 625.0; 50 595.0 360.0; 51 1340.0 725.0; | ||
| 52 1740.0 245.0; | ||
| ]; | ||
| coordinates(:,1) = []; | ||
| amount = size(coordinates,1); % ���е���Ŀ | ||
| % ͨ���������ķ������������� | ||
| dist_matrix = zeros(amount, amount); | ||
| coor_x_tmp1 = coordinates(:,1) * ones(1,amount); | ||
| coor_x_tmp2 = coor_x_tmp1'; | ||
| coor_y_tmp1 = coordinates(:,2) * ones(1,amount); | ||
| coor_y_tmp2 = coor_y_tmp1'; | ||
| dist_matrix = sqrt((coor_x_tmp1-coor_x_tmp2).^2 + ... | ||
| (coor_y_tmp1-coor_y_tmp2).^2); | ||
|
|
||
| sol_new = 1:amount; % ������ʼ�� | ||
| % sol_new��ÿ�β������½⣻sol_current�ǵ�ǰ�⣻sol_best����ȴ�е���ý⣻ | ||
| E_current = inf;E_best = inf; % E_current�ǵ�ǰ���Ӧ�Ļ�·���룻 | ||
| % E_new���½�Ļ�·���룻 | ||
| % E_best�����Ž�� | ||
| sol_current = sol_new; sol_best = sol_new; | ||
| p = 1; | ||
|
|
||
| while t>=tf | ||
| for r=1:Markov_length % Markov������ | ||
| % ��������Ŷ� | ||
| if (rand < 0.5) % ��������ǽ������������������� | ||
| % ������ | ||
| ind1 = 0; ind2 = 0; | ||
| while (ind1 == ind2) | ||
| ind1 = ceil(rand.*amount); | ||
| ind2 = ceil(rand.*amount); | ||
| end | ||
| tmp1 = sol_new(ind1); | ||
| sol_new(ind1) = sol_new(ind2); | ||
| sol_new(ind2) = tmp1; | ||
| else | ||
| % ������ | ||
| ind1 = 0; ind2 = 0; ind3 = 0; | ||
| while (ind1 == ind2) || (ind1 == ind3) ... | ||
| || (ind2 == ind3) || (abs(ind1-ind2) == 1) | ||
| ind1 = ceil(rand.*amount); | ||
| ind2 = ceil(rand.*amount); | ||
| ind3 = ceil(rand.*amount); | ||
| end | ||
| tmp1 = ind1;tmp2 = ind2;tmp3 = ind3; | ||
| % ȷ��ind1 < ind2 < ind3 | ||
| if (ind1 < ind2) && (ind2 < ind3) | ||
| ; | ||
| elseif (ind1 < ind3) && (ind3 < ind2) | ||
| ind2 = tmp3;ind3 = tmp2; | ||
| elseif (ind2 < ind1) && (ind1 < ind3) | ||
| ind1 = tmp2;ind2 = tmp1; | ||
| elseif (ind2 < ind3) && (ind3 < ind1) | ||
| ind1 = tmp2;ind2 = tmp3; ind3 = tmp1; | ||
| elseif (ind3 < ind1) && (ind1 < ind2) | ||
| ind1 = tmp3;ind2 = tmp1; ind3 = tmp2; | ||
| elseif (ind3 < ind2) && (ind2 < ind1) | ||
| ind1 = tmp3;ind2 = tmp2; ind3 = tmp1; | ||
| end | ||
|
|
||
| tmplist1 = sol_new((ind1+1):(ind2-1)); | ||
| sol_new((ind1+1):(ind1+ind3-ind2+1)) = ... | ||
| sol_new((ind2):(ind3)); | ||
| sol_new((ind1+ind3-ind2+2):ind3) = ... | ||
| tmplist1; | ||
| end | ||
|
|
||
| %����Ƿ�����Լ�� | ||
|
|
||
| % ����Ŀ�꺯��ֵ�������ܣ� | ||
| E_new = 0; | ||
| for i = 1 : (amount-1) | ||
| E_new = E_new + ... | ||
| dist_matrix(sol_new(i),sol_new(i+1)); | ||
| end | ||
| % �����ϴ����һ�����е���һ�����еľ��� | ||
| E_new = E_new + ... | ||
| dist_matrix(sol_new(amount),sol_new(1)); | ||
|
|
||
| if E_new < E_current | ||
| E_current = E_new; | ||
| sol_current = sol_new; | ||
| if E_new < E_best | ||
| % ����ȴ��������õĽⱣ������ | ||
| E_best = E_new; | ||
| sol_best = sol_new; | ||
| end | ||
| else | ||
| % ���½��Ŀ�꺯��ֵС�ڵ�ǰ��ģ� | ||
| % �����һ�����ʽ����½� | ||
| if rand < exp(-(E_new-E_current)./t) | ||
| E_current = E_new; | ||
| sol_current = sol_new; | ||
| else | ||
| sol_new = sol_current; | ||
| end | ||
| end | ||
| end | ||
| t=t.*a; % ���Ʋ���t���¶ȣ�����Ϊԭ����a�� | ||
| end | ||
|
|
||
| disp('���Ž�Ϊ��') | ||
| disp(sol_best) | ||
| disp('��̾��룺') | ||
| disp(E_best) |
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,13 @@ | ||
| u=[-1:0.05:1]; | ||
| % t=sin(2*pi*u); | ||
| t=0.5+0.5*sin(2*pi*u); | ||
| % try to use different f and training algo. | ||
| net=newff([-1 1],[10 1],{'tansig','purelin'},'trainlm'); | ||
| % training param. | ||
| net.trainParam.goal=1e-8; | ||
| net.trainParam.epochs=10000; | ||
|
|
||
| [net,tr]=train(net,u,t); | ||
| y=sim(net,u); | ||
| % plot(u,t,'b',u,y,'r'); | ||
| % nnd9sdq |
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file added
BIN
+411 KB
...法-现代的算法/神经网络/lecture 13/Adaptive Control Using Neural Networks and Approximate Models.pdf
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Oops, something went wrong.