神经网络的期末考试复习

页给出的坐标点进行二分类。代码（P127）5;%%%5;%%%%%%%%%%%%%%%%%P4P=T=11001110000001111allP=T=11001110000001111V=[221.[Q]=[Q]=2.=%3.%=%=%=4.=5.==P135）%%%%%线%plot(x,y,'o'%用%%onlegend('原始数据点','最小二乘拟合直线'%%=%%%偏置为%=%%title(用于最小二乘拟合直线'%%%randn('state'%线%%plot(x,y,'o'%用%%onlegend('原始数据点','最小二乘拟合直线'title(用于最小二乘拟合直线'%=%%%=%%P=[13;];3P=[123;21];T=331.==2.=%=%=3.===%=%4.=5.A=e=设计神经网络实现下面的函数关系。newlinP136）0.1336%=%plot(P,T,'b+','linewidth',2,'MarkerSize'%%%%%%%的b+%%%%%%%%%%%%%%set(gcf,'Position',1.==2.net=newff(minmax(P),[5,S],{'tansig','purelin'%%3.%%%%%%%4.%5.=%plot(P,T,'b+')plot(P1,A,'r')%BP神经网络（y=x2-4）yx/0x4BPall=T=%%%%%%%%在T%=%plot(P,T,'b+','linewidth',2,'MarkerSize'%%%%%%%运b+%%%%%%%%%%%%%%set(gcf,'Position',1.==2.net=newff(minmax(P),[3,S],{'tansig','purelin'},lm'trainlm）%%3.%%%%%%%4.%5.=%plot(P,T,'b+')plot(P1,A,'r')%BP%rng('default')%%%%%%点%生10为a为%%%使p%plot(p(1,tc==1),p(2,tc==1),'ro','LineWidth'plot(p(1,tc==2),p(2,tc==2),'b*','LineWidth'plot(p(1,tc==3),p(2,tc==3),'k+','LineWidth'%%%%%%%%%%legend('','','第三类'=2-4.T=T=%==net=newff(minmax(p),[9,S],{'tansig','purelin'trainlm）%%=%%5.%1===disp(['训练误差为'%2==plot(xx(1,:),xx(2,:),'go')=%3plot(xx(1,y==1),xx(2,y==1),'ro'on;plot(xx(1,y==2),xx(2,y==2),'b*'plot(xx(1,y==3),xx(2,y==3),'k+'plot(p(1,tc==1),p(2,tc==1),'ro','LineWidth'plot(p(1,tc==2),p(2,tc==2),'b*','LineWidth'plot(p(1,tc==3),p(2,tc==3),'k+','LineWidth'legend('','','第三类'title(''yx/0x4BPall=T=%%%%%%%%在T%=%plot(P,T,'b+','linewidth',2,'MarkerSize'%%%%%%%b+%%%%%%%set(gcf,'Position',%%%%%%%没1.==2-4.%=net=newrb(P,T，指定均方误差[0]，扩散速度[1][Q]，控制显示级别的参数[25]）=5.=%plot(P,T,'b+')plot(P1,A,'r')%坐标点分类，并进行结果对比。和%rng('default')%%%%%%%%点%生10为a为%%%%使p%plot(p(1,tc==1),p(2,tc==1),'ro','LineWidth'plot(p(1,tc==2),p(2,tc==2),'b*','LineWidth'plot(p(1,tc==3),p(2,tc==3),'k+','LineWidth'legend('','','第三类'=2-4.T=T==%%%%%%net=newrb(P,T，[0][1][Q]，控制显示级别的参数[25]）%=5.%1===disp(['训练误差为'%2==plot(xx(1,:),xx(2,:),'go')=%3plot(xx(1,y==1),xx(2,y==1),'ro'on;plot(xx(1,y==2),xx(2,y==2),'b*'plot(xx(1,y==3),xx(2,y==3),'k+'plot(p(1,tc==1),p(2,tc==1),'ro','LineWidth'plot(p(1,tc==2),p(2,tc==2),'b*','LineWidth'plot(p(1,tc==3),p(2,tc==3),'k+','LineWidth'legend('','','第三类'title(''SOM8-1。%采用竞争神经网络,%all.8,3.7,3.7,8.6,9.1,.4,8.0,plot(x0(1,:),x0(2,:),'bo')%=%%=a%%%%%%%net=newc(x0,a);a=fprintf('分类结果\n'%%%%%%%plot(x1(1,:),x1(2,:),'ro')plot(x2(1,:),x2(2,:),'k*')title('竞争神经网络分类结果')legend('','')%','第二类''第三类%采用竞争神经网络,%allrng('default').8,3.7,3.7,8.6,9.1,.4,8.0,figure;set(gcf,'Position',plot(x(1,:),x(2,:),'bo','linewidth'%title('待聚类的数据%%%'bo','linewidth'%plot(ww(:,1),ww(:,2),'rd','LineWidth',2,'markersize','迭代步数：'%%%竞争，=*-%=+'go','LineWidth'%'gd','LineWidth'%%第1类%第2类figure;set(gcf,'Position',plot(x1(1,:),x1(2,:),'co','linewidth'%第1类plot(x2(1,:),x2(2,:),'b*','linewidth'%第2类%plot(ww(:,1),ww(:,2),'rd','LineWidth',2,'markersize',title('竞争神经网络聚类结果')legend('','','聚类中心')自组织映射网络（SOM）.8,3.7,3.7,8.6,9.1,.4,8.0,plot(x0(1,:),x0(2,:),'bo')%%%%%%%%%net=newsom(minmax(x0),a);a%=%%版=selforgmap(a);a%=%==y=%=%%%%%plot(x1(1,:),x1(2,:),'ro')plot(x2(1,:),x2(2,:),'k*')title('竞争神经网络分类结果')legend('','')%%%%%%%%%%%title('竞争神经网络分类结果%legend('第一类','','第三类','%allplot(T(1,:),T(2,:),'ro')%%%%%===%on;plot(P(1,:),P(2,:),'b*')%=%=%%plot(T(1,:),T(2,:),'ro')%%plot(P(1,:),P(2,:),'b*')'b-'用5第6前期的股价预测，%%%%%%%%%本=%%%%%%%%n=%%%%%%%%mm%%%%%%%%%net=newelm(threshold,[0,1],[8,1],{'tansig','purelin'},'trainlm'%%%%%%%%%%%%%%%%%%%fprintf(%f\n',T=+y=+plot(6:140,T,'b-',6:1