模式识别 神经网络识别MATLAB实现

模糊神经网络模式识别

function retstr = FnnTrain(dt,ld,tt,sp)

retstr=-1;

%%%% 输入参数赋值开始%%%%%%%%%%%%%%%%%%%%%%% % 方便调试程序用，程序调试时去掉这部分的注释

% dt=4; %学习阈值

% ld=0.05; %学习进度

% tt=10; %训练次数

% sp='data\sample.txt'; %一个点的监测数据

%%%% 输入参数赋值结束%%%%%%%%%%%%%%%%%%%%%%%% global recordDimention;

global sampleNumber;

global weightNumber;

global distanceThread;

global WW;

global learningDegree;

global epochsNumber;

distanceThread=dt;

learningDegree=ld;

traintimes=tt;

A=load(sp);

[Arow Acol]=size(A);

%样本个数

sampleNumber=Arow;

recordDimention=Acol;

disp(sampleNumber);

WW=A(1,:);

WW=[WW [1]];

weightNumber=1;

epochsNumber=1;

for jj=2:1:sampleNumber

TrainNN2(A(jj,:));

end

for jt=1:traintimes-1

for jt2=1:sampleNumber

TrainNN2(A(jj,:));

end

end

% 将训练结果写入权值文件

dlmwrite('data\w.dat',WW,'\t');

%

% 训练子函数

%

function TrainNN2(a)

global recordDimention;

global sampleNumber;

global weightNumber;

global distanceThread;

global WW;

global learningDegree;

global epochsNumber;

Ldistance=zeros(2,weightNumber);

for j1=1:weightNumber

Ldistance(2,j1)=j1;

end

% %%%%%输入输出空间的模糊分割

for j2=1:weightNumber

Lx=0;

for j3=1:recordDimention

Lx=Lx + (a(j3) - WW(j2,j3)) .* (a(j3) - WW(j2,j3));

end

Ldistance(1,j2)=sqrt(Lx);

end

% %%%%%计算模糊空间的距离

Lx1=Ldistance(1,1);

Lx2=Ldistance(2,1);

for j4=2:weightNumber

if(Ldistance(1,j4)

Lx1=Ldistance(1,j4);

Lx2=Ldistance(2,j4);

end

end

% %%%%%修正模糊规则

updateW=0;

nowWeight=Lx2;

if(Lx1<=distanceThread)

for j6=1:recordDimention

WW(nowWeight,j6)=WW(nowWeight,j6) + learningDegree .* (a(j6) -

WW(nowWeight,j6));

end

WW(nowWeight,recordDimention+1)=WW(nowWeight,recordDimention+1)+1;

updateW=1;

end

if( updateW==0)

weightNumber=weightNumber+1;

a=[a [1]];

WW=[WW;a];

end

L-M神经网络算法之MATLAB实现1

%此为BP网络训练程序

function retstr = LmTrain(ModelNo,NetPara,TrainPara,InputFun,OutputFun,DataDir) NNTWARN OFF

retstr=-1;

%%%% 输入参数赋值开始%%%%%%%%%%%%%%%%%%%%%%%

% 方便调试程序用，程序调试时去掉这部分的注释

ModelNo='1';

NetPara(1)=7;

NetPara(2)=1;

NetPara(3)=6;

NetPara(4)=10;

TrainPara(1)=25;

TrainPara(2)=1000;

TrainPara(3)=0.001;

TrainPara(4)=0.001;

TrainPara(5)=0.001;

TrainPara(6)=10;

TrainPara(7)=0.1;

TrainPara(8)=1e10;

DataDir='.';

InputFun = 'tansig';

OutputFun = 'purelin';

%%%% 输入参数赋值结束%%%%%%%%%%%%%%%%%%%%%%%%

%保留原目录

olddir=pwd;

%进入数据所在目录

cd(DataDir);

deltalin(1);

deltalog(1);

deltatan(1);

% 网络参数

InputDim=NetPara(1); %输入层节点数

OutputDim=NetPara(2); %输出层节点数