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%% 清空环境变量
clc
clear
load 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;
end
end
%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=31:36
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;
end
yc=yc*100000;
y(:,1)=y(:,1)*10000;
%计算预测的每月需求量
for j=36:-1:2
ys(j)=(yc(j)-yc(j-1))/10;
end
figure(2)
plot(ys(31:36),'-*');
hold on
plot(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)
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