基于MATLAB的灰色神经网络预测订单需求

news2024/9/17 9:19:58

%% 清空环境变量
clc
clear
%%  36个样本，每个样本是6个月的销售量
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

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  

%% 绘制误差随进化次数的变化趋势
figure(1)
plot(E)
title('训练误差','fontsize',12);
xlabel('进化次数','fontsize',12);
ylabel('误差','fontsize',12);


%根据训练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('灰色神经网络预测','fontsize',12)
xlabel('月份','fontsize',12)
ylabel('销量','fontsize',12)

仿真结果为：