%% 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:n y(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_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); %误差求与 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 -r600 28-3%根据训出得灰色神经网络进行预测for i=31:36 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_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;%计算预测得每月需求量for j=36:-1:2 ys(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)
