xia_bo.rar_predict_xia_bo_小波 预测_小波神经网络_小波预测

function xiabo3; %step 1========================= %定义输入样本； x=[0.3241 0.157 0.258 0.3801 0.3921; 0.4006 0.1885 0.3138 0.3353 0.3009; 0.3994 0.165 0.2476 0.2559 0.2798; 0.4262 0.1531 0.3183 0.2865 0.3695; 0.3693 0.156 0.2906 0.3379 0.3283; 0.4805 0.134 0.2488 0.3372 0.3476; 0.3079 0.1486 0.2681 0.366 0.3923; 0.414 0.1457 0.2783 0.2918 0.2774; 0.3996 0.1563 0.2508 0.3231 0.3326; 0.3759 0.1584 0.3096 0.2874 0.3677;]; targ=[0.0177 0.0354 0.0531 0.0708 0.0885 0.1062 0.1239 0.1357 0.0826 0.0295]; eta=0.02;aerfa=0.935; %初始化连接权wjh(输出层和隐层的连接权);whi(隐层和输出层的连接权); %假设小波函数节点数为：H个；样本数为P; %输出节点数为:J个；输入节点数为：I个； H=10;P=10; I=5; J=H; %初始化小波参数 b=rand(H,1); a=rand(H,1); %初始化权系数； whi=rand(I,H); wjh=rand(H,J); %阈值初始化; b1=rand(H,1); b2=rand(J,1); p=0; %保存的误差； Err_NetOut=[]; flag=1;count=0; while flag>0 flag=0; count=count+1; %step 2================================= 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 %step 3==================================== 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 %step 6==保存每次误差===== wuchayy=1/2*sumsqr(oxjp-targ); %E_x=1/2*sumsqr(x); Err_NetOut=[Err_NetOut wuchayy];%保存每次的误差; %Err_rate=Err_NetOut/E_x; %Err_rate %oxjp %求detaj ,detab2================================== 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; %求detah, detawhi detab1 detab detaa;======================== 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，修正各个系数========================================== 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'; %====================================================== %引入修正算法！！ %判断所有的样本是否计算完================================== p=p+1; if p~=P flag=flag+1; else if Err_NetOut(end)>0.01 flag=flag+1; else figure; plot(Err_NetOut); title('误差曲线'); disp('目标达到'); disp(oxjp); end end if count>500 figure; plot(Err_NetOut); title('误差曲线'); disp('故障点距离'); disp(oxjp); break; end end %这里还需要定义三个函数 %代码: function y3=diffai(x); y3=-0.75*sin(1.75*x)*exp(-x.^2/2)-cos(1.75*x)*exp(-x.^2/2)*x; %代码: function yl=fai(x) yl=cos(0.75.*x)+exp(-x.^2/2); %代码: function y2=fnn(x) y2=1/(1+exp(-x));