神经网络3-6-2结构

神经网络为3-6-2结构，权值Wij 、Wjl 的初始值取[-1，+1]之间的随机值，取学习率η=0.50，动量因子α=0.05。 网络训练程序

clear all;

close all; xite=0.50; alfa=0.05; w2=rands(6,2); w2_1=w2;w2_2=w2_1; w1=rands(3,6); w1_1=w1;w1_2=w1; dw1=0*w1;

I=[0,0,0,0,0,0]';

Iout=[0,0,0,0,0,0]'; FI=[0,0,0,0,0,0]'; OUT=2; k=0;

E=1.0; NS=3; while E>=1e-020

k=k+1; times(k)=k; for s=1:1:NS %MIMO Samples

xs=[1,0,0; 0,1,0; 0,0,1]; %Ideal Input ys=[1,0; 0,0.5; 0,1]; %Ideal Output x=xs(s,:);

for j=1:1:6 I(j)=x*w1(:,j); Iout(j)=1/(1+exp(-I(j))); end yl=w2'*Iout; yl=yl'; el=0; y=ys(s,:);

for l=1:1:OUT

el=el+0.5*(y(l)-yl(l))^2; %Output error

end

E=0;

if s==NS

for s=1:1:NS

E=E+el;

end

end 初始化V 、W 、E m in ,、η q=1，p=1,E =0

输入样本，计算各层输出： m 21j f y T j j ,...,,),(==X V l 21k f o T j k ,...,,),(==Y W 计算误差： ∑∑==-=P 1p l 1k 2k k o d 21E )(

计算各层误差信号： l 21k o o 1o d k k k k ,...,,,))((δo k =--=

m 21j y y 1w j j l 1k jk o k y j ,...,,))((=-=∑

=δδ 调整各层权值： m

10j l 21k j y o k jk w jk w ,...,,,...,,,,==+?ηδ n 10x m 21j x v v i y j ij ij ,...,,,...,,,==+?ηδ

Y p 增1,q 增1 p

结束

el=y-yl;

w2=w2_1+xite*Iout*el+alfa*(w2_1-w2_2);

for j=1:1:6

S=1/(1+exp(-I(j)));

FI(j)=S*(1-S);

end

for i=1:1:3

for j=1:1:6

dw1(i,j)=xite*FI(j)*x(i)*(el(1)*w2(j,1)+el(2)*w2(j,2));

end

end

w1=w1_1+dw1+alfa*(w1_1-w1_2);

w1_2=w1_1;w1_1=w1;

w2_2=w2_1;w2_1=w2;

end %End of for

Ek(k)=E;

end %End of while

figure(1);

plot(times,Ek,'r');

xlabel('k');ylabel('E');

save wfile w1 w2;

网络测试程序

%Test BP

clear all;

load wfile w1 w2;

%N Samples

x=[0.970,0.001,0.001;

0.000,0.980,0.000;

0.002,0.000,1.040;

0.500,0.500,0.500;

1.000,0.000,0.000;

0.000,1.000,0.000;

0.000,0.000,1.000];

for i=1:1:7

for j=1:1:6

I(i,j)=x(i,:)*w1(:,j);

Iout(i,j)=1/(1+exp(-I(i,j)));

end

end

y=w2'*Iout';

y=y'