2专题 精通灰色神经网络通过三个matlab建模案例.7z

%% 学习目标：基于灰色神经网络的预测算法 clear all clc %设置总的样本 X =[1.5220 0.5485 0.8680 0.6854 0.9844 0.5773; 1.4310 0.5943 0.7612 0.6567 0.9510 0.7184; 1.6710 0.6346 0.7153 0.6802 0.9494 0.6230; 1.7750 0.7838 0.8895 0.7442 0.9291 0.6924; 1.6300 0.5182 0.8228 0.6335 0.8668 0.5831; 1.6700 0.7207 0.8897 0.6690 0.9516 0.7863; 1.5920 0.6480 0.6915 0.7347 0.8530 0.4497; 2.0410 0.7291 0.9309 0.6788 0.9968 0.7356; 1.6310 0.7753 0.7970 0.7228 0.8702 0.7679; 2.0280 0.7923 0.8961 0.6363 0.9478 0.8039; 1.5860 0.7491 0.8884 0.6658 0.9398 0.8797; 1.7160 0.7550 0.7602 0.6157 0.9134 0.7204; 1.5110 0.5498 0.8127 0.6204 0.9284 0.6145; 1.4550 0.5404 0.7486 0.6328 0.9591 0.6857; 1.5680 0.6182 0.7471 0.6585 0.9802 0.6368; 1.8830 0.7931 0.9681 0.7646 0.8886 0.7411; 1.5620 0.5496 0.8658 0.7181 0.7832 0.5669; 1.6900 0.6644 0.8992 0.6357 0.9087 0.7933; 1.7910 0.5768 0.7130 0.7730 0.8829 0.4907; 2.0190 0.7473 0.9531 0.6768 0.9964 0.8092; 1.8520 0.8236 0.8079 0.6796 0.9272 0.8512; 1.5390 0.8640 0.8862 0.6386 0.9685 0.8567; 1.7280 0.7814 0.9410 0.6944 0.9629 0.8775; 1.6760 0.7285 0.7868 0.6987 0.8805 0.7630; 1.6670 0.5476 0.8223 0.6286 0.9355 0.5898; 1.3510 0.5557 0.7072 0.6811 0.9553 0.7326; 1.6030 0.5519 0.6816 0.7009 0.9736 0.6151; 1.8760 0.8039 0.8852 0.8068 0.9644 0.7477; 1.6310 0.4490 0.7941 0.7138 0.8281 0.5306; 1.7500 0.6729 0.8526 0.6223 0.9452 0.7562; 1.6000 0.6012 0.6640 0.7920 0.8878 0.4979; 1.9460 0.7751 0.9155 0.7032 0.9168 0.7432; 1.6360 0.7931 0.7635 0.6393 0.8757 0.7692; 1.8650 0.7598 0.8426 0.6756 0.9234 0.8065; 1.8140 0.7342 0.7572 0.6134 0.8862 0.7907]; [aa,bb]=size(X); %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; %设置样品数 cc=30; %循环迭代 for j=1:20 %循环迭代 E(j)=0; for i=1:cc %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'); xlabel('进化次数'); ylabel('误差'); %根据训练出的灰色神经网络进行样本预测 for i=(cc+1):aa 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*1000; y(:,1)=y(:,1)*100; %计算预测值 for j=aa:-1:2 ys(j)=(yc(j)-yc(j-1))/10; end figure(2) plot(ys((cc+1):aa),'-*'); hold on plot(X((cc+1):aa,1)*100,'r:o'); legend('神经网络预测值','实际样本值') title('灰色系统预测') %计算平均相对误差 xderror=0; for i=aa:-1:cc xderror=xderror+abs(ys(i)-X(i,1))/yc(i); end xderror=xderror/(aa-cc)