1、% 清空环境变量clcclearload data% 数据累加作为网络输入n,m=size(X);for i=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
2、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+
3、b5*y(1,6)/a-y(1,1);kk=1;% 循环迭代for j=1:10%循环迭代E(j)=0;for i=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_
4、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
5、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_
6、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 -r600 28-3%根据训出的灰色神经网络进行预测for i=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
7、_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for j=36:-1:2ys(j)=(yc(j)-yc(j-1)/10;endfigure(2)plot(ys(31:36),-*);hold onplot(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)
