人工智能之神经网络代码22

#pragmahdrstop#include#includeconstA=30.0;constB=10.0;constMAX=500;/最大训练次数constCOEF=0.0035;/网络的学习效率constBCOEF=0.001;/网络的阀值调整效率constERROR=0.002;/网络训练中的允许误差constACCURACY=0.0005;/网络要求精度doublesample414=0,0,0,0,5,1,4,19.020,5,3,3,14.150,5,5,2,14.360,5,3,3,14.150,5,3,2,15.390,5,3,2,15.390,5,5,1,19.680,5,1,2,21.060,5,3,3,14.150,5,5,4,12.680,5,5,2,14.360,5,1,3,19.610,5,3,4,13.650,5,5,5,12.430,5,1,4,19.020,5,1,4,19.020,5,3,5,13.390,5,5,4,12.680,5,1,3,19.610,5,3,2,15.390,1,3,1,11.110,1,5,2,6.521,1,1,3,10.190,1,3,4,6.043,1,5,5,5.242,1,5,3,5.724,1,1,4,9.766,1,3,5,5.870,1,5,4,5.406,1,1,3,10.190,1,1,5,9.545,1,3,4,6.043,1,5,3,5.724,1,1,2,11.250,1,3,1,11.110,1,3,3,6.380,1,5,2,6.521,1,1,1,16.000,1,3,2,7.219,1,5,3,5.724;doublew41010,wc41010,b410,bc410;doubleo410,netin410,d410,differ;/单个样本的误差doubleis;/全体样本均方差intcount,a;voidnetout(intm,intn);/计算网络隐含层和输出层的输出voidcalculd(intm,intn);/计算网络的反向传播误差voidcalcalwc(intm,intn);/计算网络权值的调整量voidcalcaulbc(intm,intn);/计算网络阀值的调整量voidchangew(intm,intn);/调整网络权值voidchangeb(intm,intn);/调整网络阀值voidclearwc(intm,intn);/清除网络权值变化量wcvoidclearbc(intm,intn);/清除网络阀值变化量bcvoidinitialw(void);/初始化NN网络权值Wvoidinitialb(void);/初始化NN网络阀值voidcalculdiffer(void);/计算NN网络单个样本误差voidcalculis(void);/计算NN网络全体样本误差voidtrainNN(void);/训练NN网络/*计算NN网络隐含层和输出层的输出*/voidnetout(intm,intn)inti,j,k;/隐含层各节点的的输出for(j=1,i=2;j=m;j+)/m为隐含层节点个数netinij=0.0;for(k=1;k=3;k+)/隐含层的每个节点均有三个输入变量netinij=netinij+oi-1k*wikj;netinij=netinij-bij;oij=A/(1+exp(-netinij/B);/输出层各节点的输出for(j=1,i=3;j=n;j+)netinij=0.0;for(k=1;k=m;k+)netinij=netinij+oi-1k*wikj;netinij=netinij-bij;oij=A/(1+exp(-netinij/B)/*计算NN网络的反向传播误差*/voidcalculd(intm,intn)inti,j,k;doublet;a=count-1;d31=(o31-samplea3)*(A/B)*exp(-netin31/B)/pow(1+exp(-netin31/B),2);/隐含层的误差for(j=1,i=2;j=m;j+)t=0.00;for(k=1;k=n;k+)t=t+wi+1jk*di+1k;dij=t*(A/B)*exp(-netinij/B)/pow(1+exp(-netinij/B),2);/*计算网络权值W的调整量*/voidcalculwc(intm,intn)inti,j,k;/输出层（第三层）与隐含层（第二层）之间的连接权值的调整for(i=1,k=3;i=m;i+)for(j=1;j=n;j+)wckij=-COEF*dkj*ok-1i+0.5*wckij;/printf(n);/隐含层与输入层之间的连接权值的调整for(i=1,k=2;i=m;i+)for(j=1;j=m;j+)wckij=-COEF*dkj*ok-1i+0.5*wckij;/printf(n);/*计算网络阀值的调整量*/voidcalculbc(intm,intn)intj;for(j=1;j=m;j+)bc2j=BCOEF*d2j;for(j=1;j=n;j+)bc3j=BCOEF*d3j;/*调整网络权值*/voidchangw(intm,intn)inti,j;for(i=1;i=3;i+)for(j=1;j=m;j+)w2ij=0.9*w2ij+wc2ij;/为了保证系统有较好的鲁棒性，计算权值时乘惯性系数0.9printf(w2%d%d=%fn,i,j,w2ij);for(i=1;i=m;i+)for(j=1;j=n;j+)w3ij=0.9*w3ij+wc3ij;printf(w3%d%d=%fn,i,j,w3ij);/*调整网络阀值*/voidchangb(intm,intn)intj;for(j=1;j=m;j+)b2j=b2j+bc2j;for(j=1;j=n;j+)b3j=b3j+bc3j;/*清除网络权值变化量wc*/voidclearwc(void)for(inti=0;i4;i+)for(intj=0;j10;j+)for(intk=0;k10;k+)wcijk=0.00;/*清除网络阀值变化量*/voidclearbc(void)for(inti=0;i4;i+)for(intj=0;j10;j+)bcij=0.00;/*初始化网络权值W*/voidinitialw(void)inti,j,k,x;doubleweight;for(i=0;i4;i+)for(j=0;j10;j+)for(k=0;k10;k+)randomize();x=100+random(400);weight=(double)x/5000.00;wijk=weight;/*初始化网络阀值*/voidinitialb(void)inti,j,x;doublefazhi;for(i=0;i4;i+)for(j=0;j10;j+)randomize();for(intk=0;k12;k+)x=100+random(400);fazhi=(double)x/50000.00;bij=fazhi;/*计算网络单个样本误差*/voidcalculdiffer(void)a=count-1;differ=0.5*(o31-samplea3)*(o31-samplea3);voidcalculis(void)inti;is=0.0;for(i=0;i=19;i+)o11=samplei0;o12=samplei1;o13=samplei2;netout(8,1);is=is+(o31-samplei3)*(o31-samplei3);is=is/20;/*训练网络*/voidtrainNN(void)longinttime;inti,x4;initialw();initialb();for(time=1;time=MAX;time+)count=0;while(countERROR)calculd(8,1);calculwc(8,1);calculbc(8,1);changw(8,1);changb(8,1);netout(8,1);calculdiffer();printf(Thisis%dtimestrainingNN.n,time);calculis();printf(is=%fn,is);if(isACCURACY)break;/-#pragmaargsusedintmain(intargc,char*argv)doubleresult;intm,test4;charch=y;coutPleasewaitforthetrainofNN:endl;trainNN();coutNow,thismodularnetworkcanworkforyou.endl;while(ch=y|ch=Y)cou