kk.rar_www.nn.kk.c

global net N_num dimension all_p all_t telno right_num wrong_num; % N Application M-file for n.fig % FIG = N launch n GUI. % N('callback_name', ...) invoke the named callback. % Last Modified by GUIDE v2.0 16-Jan-2003 16:31:59 %开始初始化网络 dimension=3032; %在网络中向量的维数 N_num=134; %神经元个数 mc=6; %网络输出向量的维数 charset='ABCDEFG'; rng=[]; n=0; right_num=0; wrong_num=0; for n=1:dimension rng=[rng ; [0 1]]; %每个向量的输入范围 end net.inputs{1}.range=rng; net=newff(rng,[N_num mc],{'logsig' 'logsig' });%建立一个网络 %net=newff(rng,[N_num mc],{'logsig' 'logsig' });%建立一个网络 net.inputs{1}.size=dimension; init(net); net.trainFcn='traingda'; net.trainParam.goal=0.0000001; net.trainParam.epochs=600; net.trainParam.Gradient=1e-7; net.trainParam.show=5; net.trainParam.lr=0.05; net.trainParam.lr_inc=1.33; net.trainParam.lr_dec=0.5; %生成训练用的数据(2类） load sp.dat sp; load qt.dat qt; load sy.dat sy; load sh.dat sh; load ss.dat ss; load zp.dat zp; [sp_row,colum]=size(sp); [qt_row,colum]=size(qt); [sy_row,colum]=size(sy); [sh_row,colum]=size(sh); [ss_row,colum]=size(ss); [zp_row,colum]=size(zp); same_num=53; if sp_row>same_num sp_row=same_num; end if qt_row>same_num qt_row=same_num; end if sy_row>same_num sy_row=same_num; end if sh_row>same_num sh_row=same_num; end if ss_row>same_num ss_row=same_num; end if zp_row>same_num sp_row=same_num; end getrow=[sp_row qt_row sy_row sh_row ss_row zp_row]; minrow=min(getrow); qt(:,colum+1)=1; qt(:,colum+2)=0; qt(:,colum+3)=0; qt(:,colum+4)=0; qt(:,colum+5)=0; qt(:,colum+6)=0; sh(:,colum+1)=0; sh(:,colum+2)=1; sh(:,colum+3)=0; sh(:,colum+4)=0; sh(:,colum+5)=0; sh(:,colum+6)=0; sp(:,colum+1)=0; sp(:,colum+2)=0; sp(:,colum+3)=1; sp(:,colum+4)=0; sp(:,colum+5)=0; sp(:,colum+6)=0; ss(:,colum+1)=0; ss(:,colum+2)=0; ss(:,colum+3)=0; ss(:,colum+4)=1; ss(:,colum+5)=0; ss(:,colum+6)=0; sy(:,colum+1)=0; sy(:,colum+2)=0; sy(:,colum+3)=0; sy(:,colum+4)=0; sy(:,colum+5)=1; sy(:,colum+6)=0; zp(:,colum+1)=0; zp(:,colum+2)=0; zp(:,colum+3)=0; zp(:,colum+4)=0; zp(:,colum+5)=0; zp(:,colum+6)=1; sp=sp'; qt=qt'; sy=sy'; sh=sh'; ss=ss'; zp=zp'; all_p=[]; dj=0; for a1=1:1:minrow dj=dj+1; all_p=[all_p qt(:,dj)]; all_p=[all_p sh(:,dj)]; all_p=[all_p sp(:,dj)]; all_p=[all_p ss(:,dj)]; all_p=[all_p sy(:,dj)]; all_p=[all_p zp(:,dj)]; end dj=minrow; for a1=1:1:qt_row-minrow dj=dj+1; all_p=[all_p qt(:,dj)]; end dj=minrow; for a1=1:1:sh_row-minrow dj=dj+1; all_p=[all_p sh(:,dj)]; end dj=minrow; for a1=1:1:sp_row-minrow dj=dj+1; all_p=[all_p sp(:,dj)]; end dj=minrow; for a1=1:1:ss_row-minrow dj=dj+1; all_p=[all_p ss(:,dj)]; end dj=minrow; for a1=1:1:sy_row-minrow dj=dj+1; all_p=[all_p sy(:,dj)]; end dj=minrow; for a1=1:1:zp_row-minrow dj=dj+1; all_p=[all_p zp(:,dj)]; end [p_row,p_col]=size(all_p); %telno=all_p(1:2,:); %all_p(1:2,:)=[]; %all_t=all_p(colum-6:colum-5:colum-4:colum-3:colum-2:colum-1:colum,:); all_t=[]; all_t=[all_t all_p(colum+1,:)']; all_t=[all_t all_p(colum+2,:)']; all_t=[all_t all_p(colum+3,:)']; all_t=[all_t all_p(colum+4,:)']; all_t=[all_t all_p(colum+5,:)']; all_t=[all_t all_p(colum+6,:)']; all_t=all_t'; %all_t=all_p(colum+1:colum+2:colum+3:colum+4:colum+5:colum+6:colum+7,:) %all_p(colum+1 colum+2 colum+3 colum+4 colum+5 colum+6 colum+7,:)=[]; all_p(colum+6,:)=[]; all_p(colum+5,:)=[]; all_p(colum+4,:)=[]; all_p(colum+3,:)=[]; all_p(colum+2,:)=[]; all_p(colum+1,:)=[]; [net tr]=train(net,all_p,all_t); %训练网络 %save net.mat net; fid=fopen('netpar.dat','w'); fwrite(fid,net.inputs{1}.size,'int'); fwrite(fid,net.layers{1}.size,'int'); fwrite(fid,net.layers{2}.size,'int'); fwrite(fid,net.IW{1,1},'double'); fwrite(fid,net.LW{2,1},'double'); fwrite(fid,net.b{1},'double'); fwrite(fid,net.b{2},'double'); fclose(fid);