%m是自变量因素行数，n是因变量因素行数，o是所要预测用的自变量因素行数，一般m==owarning off;format long g;[filename filepath]=uigetfile('*.xlsx','选择神经网络需要参考训练的样本数据');
xlsx=[filepath filename];XLSX=xlsread(xlsx);[row column]=size(XLSX);samnum=column; %暂时选择数据列数作为样本数量和训练数量testsamnum=column;forcastsam=o;hiddenunit=8;indim=M;outdim=N;p=XLSX(1:M,:);t=XLSX(M+1:row,:);[samin,minp,maxp,tn,mint,maxt]=premnmx(p,t);rand('state',sum(100*clock));noisevar=0.01;noise=noisevar*randn(o,samnum);samout=tn+noise;testsamin=samin;testsamout=samout;maxepochs=50000;%最大训练次数learnrate=0.035;%学习速率maxerror=0.65*10^(-3);w1=0.5*rand(hiddenunit,M)-0.1;B1=0.5*rand(hiddenunit,1)-0.1;w2=0.5*rand(N,hiddenunit)-0.1;B2=0.5*rand(N,1)-0.1;errhistory=[];for i=1:maxepochs;hiddenout=logsig(w1*samin+repmat(B1,1,samnum));networkout=w2*hiddenout+repmat(B2,1,samnum);error=samout-networkout;sse=sumsqr(error);errhistory=[errhistory sse];if sse<maxerror,break,end<span style=" %------------------神经网络核心--------------------%
delta2=error;delta1=w2'*delta2.*hiddenout.*(1-hiddenout);dw2=delta2*hiddenout';dB2=delta2*ones(samnum,1);dw1=delta1*samin';dB1=delta1*ones(samnum,1);<span style=" %-------------权值/阀值完成重新动态调整--------------%w2=w2+learnrate*dw2;B2=B2+learnrate*dB2;w1=w1+learnrate*dw1;B1=B1+learnrate*dB1;enddisp(['神经网络已经完成训练，请导入预测数据'])hiddenout=logsig(w1*samin+repmat(B1,1,testsamnum));anew=w2*hiddenout+repmat(B2,1,testsamnum);ANS=postmnmx(anew,mint,maxt);%绘制x=1:column;plot(x,ANS,'o',x,t,'b--+')[filename2 filepath2]=uigetfile('*.xlsx','请选择神经网络需要预测的样本数据');xlsx2=[filepath2 filename2];XLSX2=xlsread(xlsx2);pnew=tramnmx(XLSX2,minp,maxp);hiddenout=logsig(w1*pnew+repmat(B1,1,forcastsam));anew=w2*hiddenout+repmat(B2,1,forcastsam);answer=postmnmx(anew,mint,maxt) %输出预测数据%以下函数实现最优化功能，不需要可直接删去[row2 column2]=size(pnew);zero=eye(row2);zero2=eye(row2);A=pnewfor i=1:Mzero(i,i)=1.01;zero2(i,i)=0.09;testnum1=0;testnum2=0;B=zero*A;C=zero2*A;for j=1:10000B=zero*A;C=zero2*A;%如果说在对目前参数值左右变化时输出结果总不是最大，则循环继续if postmnmx(w2*logsig(w1*A+repmat(B1,1,forcastsam))+repmat(B2,1,forcastsam),mint,maxt)<postmnmx(w2*logsig(w1*B...+repmat(B1,1,forcastsam))+repmat(B2,1,forcastsam),mint,maxt)||...postmnmx(w2*logsig(w1*A+repmat(B1,1,forcastsam))+repmat(B2,1,forcastsam),mint,maxt)<...postmnmx(w2*logsig(w1*C+repmat(B1,1,forcastsam))+repmat(B2,1,forcastsam),mint,maxt)if postmnmx(w2*logsig(w1*B+repmat(B1,1,forcastsam))+repmat(B2,1,forcastsam),mint,maxt)>...postmnmx(w2*logsig(w1*A+repmat(B1,1,forcastsam))+repmat(B2,1,forcastsam),mint,maxt)A=B;testnum1=testnum1+1;elseif postmnmx(w2*logsig(w1*A+repmat(B1,1,forcastsam))+repmat(B2,1,forcastsam),mint,maxt)>...postmnmx(w2*logsig(w1*C+repmat(B1,1,forcastsam))+repmat(B2,1,forcastsam),mint,maxt)A=C;testnum2=tsetnum2+1;endelse break;endendzero(i,i)=1;zero2(i,i)=1;endpostmnmx(A,minp,maxp)testnum1testnum2end
