灰色神经网络的预测算法

%%清空环境变量clcclearloaddata%%数据累加作为网络输入[n,m]=size(X);fori=1:ny(i,1)=sum(X(1:i,1));y(i,2)=sum(X(1:i,2));y(i,3)=sum(X(1:i,3));y(i,4)=sum(X(1:i,4));y(i,5)=sum(X(1:i,5));y(i,6)=sum(X(1:i,6));end%%网络参数初始化a=0.3+rand(1)/4;b1=0.3+rand(1)/4;b2=0.3+rand(1)/4;b3=0.3+rand(1)/4;b4=0.3+rand(1)/4;b5=0.3+rand(1)/4;%%学习速率初始化u1=0.0015;u2=0.0015;u3=0.0015;u4=0.0015;u5=0.0015;%%权值阀值初始化t=1;w11=a;w21=-y(1,1);w22=2*b1/a;w23=2*b2/a;w24=2*b3/a;w25=2*b4/a;w26=2*b5/a;w31=1+exp(-a*t);w32=1+exp(-a*t);w33=1+exp(-a*t);w34=1+exp(-a*t);w35=1+exp(-a*t);w36=1+exp(-a*t);theta=(1+exp(-a*t))*(b1*y(1,2)/a+b2*y(1,3)/a+b3*y(1,4)/a+b4*y(1,5)/a+b5*y(1,6)/a-y(1,1));kk=1;%%循环迭代forj=1:10%循环迭代E(j)=0;fori=1:30%%网络输出计算t=i;LB_b=1/(1+exp(-w11*t));%LB层输出LC_c1=LB_b*w21;%LC层输出LC_c2=y(i,2)*LB_b*w22;%LC层输出LC_c3=y(i,3)*LB_b*w23;%LC层输出LC_c4=y(i,4)*LB_b*w24;%LC层输出LC_c5=y(i,5)*LB_b*w25;%LC层输出LC_c6=y(i,6)*LB_b*w26;%LC层输出LD_d=w31*LC_c1+w32*LC_c2+w33*LC_c3+w34*LC_c4+w35*LC_c5+w36*LC_c6;%LD层输出theta=(1+exp(-w11*t))*(w22*y(i,2)/2+w23*y(i,3)/2+w24*y(i,4)/2+w25*y(i,5)/2+w26*y(i,6)/2-y(1,1));%阀值ym=LD_d-theta;%网络输出值yc(i)=ym;%%权值修正error=ym-y(i,1);%计算误差E(j)=E(j)+abs(error);%误差求和error1=error*(1+exp(-w11*t));%计算误差error2=error*(1+exp(-w11*t));%计算误差error3=error*(1+exp(-w11*t));error4=error*(1+exp(-w11*t));error5=error*(1+exp(-w11*t));error6=error*(1+exp(-w11*t));error7=(1/(1+exp(-w11*t)))*(1-1/(1+exp(-w11*t)))*(w21*error1+w22*error2+w23*error3+w24*error4+w25*error5+w26*error6);%修改权值w22=w22-u1*error2*LB_b;w23=w23-u2*error3*LB_b;w24=w24-u3*error4*LB_b;w25=w25-u4*error5*LB_b;w26=w26-u5*error6*LB_b;w11=w11+a*t*error7;endend%画误差随进化次数变化趋势figure(1)plot(E)title('训练误差','fontsize',12);xlabel('进化次数','fontsize',12);ylabel('误差','fontsize',12);%print-dtiff-r60028-3%根据训出的灰色神经网络进行预测fori=31:36t=i;LB_b=1/(1+exp(-w11*t));%LB层输出LC_c1=LB_b*w21;%LC层输出LC_c2=y(i,2)*LB_b*w22;%LC层输出LC_c3=y(i,3)*LB_b*w23;%LC层输出LC_c4=y(i,4)*LB_b*w24;%LC层输出LC_c5=y(i,5)*LB_b*w25;LC_c6=y(i,6)*LB_b*w26;LD_d=w31*LC_c1+w32*LC_c2+w33*LC_c3+w34*LC_c4+w35*LC_c5+w36*LC_c6;%LD层输出theta=(1+exp(-w11*t))*(w22*y(i,2)/2+w23*y(i,3)/2+w24*y(i,4)/2+w25*y(i,5)/2+w26*y(i,6)/2-y(1,1));%阀值ym=LD_d-theta;%网络输出值yc(i)=ym;endyc=yc*100000;y(:,1)=y(:,1)*10000;%计算预测的每月需求量forj=36:-1:2ys(j)=(yc(j)-yc(j-1))/10;endfigure(2)plot(ys(31:36),'-*');holdonplot(X(31:36,1)*10000,'r:o');legend('灰色神经网络','实际订单数')title('灰色系统预测','fontsize',12)xlabel('月份','fontsize',12)ylabel('销量','fontsize',12)