matlab现代的算法小波分析

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));