BP神经网络进行网络训练

BP神经网络进行网络训练

close all

clear

echo on

clc

I0=imread('D:\迅雷下载\bp1\0.jpg');

I0=imresize(I0,[24 9]);

I1=imread('D:\迅雷下载\bp1\1.JPG');

I1=imresize(I1,[24 9]);

I2=imread('D:\迅雷下载\bp1\2.jpg');

I2=imresize(I2,[24 9]);

I3=imread('D:\迅雷下载\bp1\3.jpg');

I3=imresize(I3,[24 9]);

I5=imread('D:\迅雷下载\bp1\5.JPG');

I5=imresize(I5,[24 9]);

I6=imread('D:\迅雷下载\bp1\6.jpg');

I6=imresize(I6,[24 9]);

I7=imread('D:\迅雷下载\bp1\7.jpg');

I7=imresize(I7,[24 9]);

I8=imread('D:\迅雷下载\bp1\8.jpg');

I8=imresize(I8,[24 9]);

I9=imread('D:\迅雷下载\bp1\9.jpg');

I9=imresize(I9,[24 9]);

p0=zeros(216,9);

I=[I0,I1,I2,I3,I5,I6,I7,I8,I9];

for k=0:8

for m=0:8

p0(m*24+1:(m+1)*24,k+1)=I(1:24,m+1+9*k);

end

end

t0=[0 1 2 3 5 6 7 8 9];

% P 为输入矢量

% T 为目标矢量

P=p0;

T=t0;

net_1=newff(minmax(P),[30 1],{'logsig' 'purelin'},'traingdm','learngdm');

% 当前网络层权值和阈值

%layerWeights=net_1.LW{2,1} ;

%layerbias=net_1.b{2} ;

% 设置训练参数

net_1.trainParam.show=50;

net_1.trainParam.lr=0.01;

%net_1.trainParam.mc=0.9;

net_1.trainParam.epochs=500;

net_1.trainParam.goal=0.05;

%训练

[net_1,tr]=train(net_1,P,T);

for m=0:8

cc0(m*24+1:(m+1)*24,1)=I1(1:24,m+1);

end

A=sim(net_1,cc0)