bp l-m.m

基于MATLAB的神经网络BP算法的改进Lm算法

clf reset
figure(gcf)
colordef(gcf,'none')
setfsize(500,200);
echo on
clc
pause 
clc
P=-1:.1:1;
T=[-0.9602 -0.5770 -0.0729 0.3771 0.6405 0.6600 0.4609... 0.1336 -0.2013 -0.4344 -0.5000 -0.3930 -0.1647 0.0988 ... 0.3072 0.3960 0.3449 0.1816 -0.0312 -0.2183 -0.3201];
pause
clc
plot(P,T,'+');
title('Training Vectors');
xlabel('Input Vector P');
ylabel('Target Vector T');
pause
clc
S1=5;
[w1,b1,w2,b2]=initff(P,S1,'tansig',T,'purelin');
echo off
k=pickic;
if k==2
    w1=[3.5000;3.5000;3.5000;3.5000;3.5000];
    b1=[2.8562;1.0774;-0.5880;1.4083;2.8722];
    w2=[0.2622 -0.2375 -0.4525 0.261 -0.1718];
    b2=[0.1326];
end
ccho on
clc
df=10;
me=8000;
eg=0.02;
tp=[df me eg ];
[w1,b1,w2,b2,ep,tr]=trainlm(w1,b1,'tansig',w2,b2,'purelin',P,T,tp);
pause
clc
plottr(tr,eg);
pause
clc
p=0.5;
a=simuff(p,w1,b1,'tansig',w2,b2,'purelin')
echo off
disp('End of DEMOLM1')