%神經(jīng)網(wǎng)絡(luò)進(jìn)行字符識(shí)別
close?all
clear
echo?on
clc
%用BP神經(jīng)網(wǎng)絡(luò)進(jìn)行英文字符識(shí)別
%NEWFF——生成一個(gè)新的bp神經(jīng)網(wǎng)絡(luò)
%TRAIN——對(duì)神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練
%SIM——對(duì)神經(jīng)網(wǎng)絡(luò)進(jìn)行仿真
pause?%?敲任意鍵開(kāi)始
clc
%載入訓(xùn)練樣本
[alphabettargets]=prprob
[R1Q1]=size（alphabet）
[R2Q2]=size（targets）
pause
clc
%生成神經(jīng)網(wǎng)絡(luò)
S1=10
S2=R2
net=newff（minmax（alphabet）[S1?S2]{‘logsig‘?‘logsig‘}‘traingdx‘）;
net.LW{21}=net.LW{21}*0.01;
pause
clc
%訓(xùn)練神經(jīng)網(wǎng)絡(luò)
%1.用理想樣本訓(xùn)練神經(jīng)網(wǎng)絡(luò)
P=alphabet;
T=targets;
%net.performFcn=‘see‘;
net.trainParam.goal=0.1;
net.trainParam.show=20;
net.trainParam.epochs=5000;
net.trainParam.mc=0.095;
[nettr]=train（netPT）;
pause
clc
%2.用喊有不同程度噪聲的樣本訓(xùn)練神經(jīng)網(wǎng)絡(luò)
netn=net;
netn.trainParam.goal=0.6;
netn.trainPAram.epochs=300;
T=[targets?targets?targets?targets];
for?pass=1:10
fprintf（‘Pass=%.0f\n‘pass）;
P=[alphabetalphabet（alphabet+randn（R1Q1）*0.1）（alphabet+randn（R1Q1）*0.2）];
[netntr]=train（netnPT）;
echo?off
end
echo?on
pause
%3.再次用理想樣本訓(xùn)練神經(jīng)網(wǎng)絡(luò)
netn.trainParam.goal=0.1;
netn.trainParam.epochs=500;
netn.trainParam.show=5;
P=alphabet;
T=targets;
[netntr]=train（netnPT）;
%訓(xùn)練