FnnTrain.rar_模糊神经_模糊神经网络_神经网络参数

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) 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