BP神经网络算法java实现package backp;import java.*;import java.awt.*;import java.io.*;import java.util.Scanner;//by realmagicianimport org.omg.CORBA.portable.InputStream;public class backpro {public static void main(String args[]){String filename=new String("delta.in");try {FileInputStream fileInputStream=new FileInputStream(filename);Scanner sinScanner=new Scanner(fileInputStream);int attN,hidN,outN,samN;attN=sinScanner.nextInt();outN=sinScanner.nextInt();hidN=sinScanner.nextInt();samN=sinScanner.nextInt();//System.out.println(attN+" "+outN+" "+hidN+" "+samN);double samin[][]=new double[samN][attN];double samout[][]=new double[samN][outN];for(int i=0;i<samN;++i){for(int j=0;j<attN;++j){samin[i][j]=sinScanner.nextDouble();}for(int j=0;j<outN;++j){samout[i][j]=sinScanner.nextDouble();}}int times=10000;double rate=0.5;BP2 bp2=new BP2(attN,outN,hidN,samN,times,rate);bp2.train(samin, samout);for(int i=0;i<hidN;++i){for(int j=0;j<attN;++j)System.out.print(bp2.dw1[i][j]+" ");System.out.println();}for(int i=0;i<outN;++i){for(int j=0;j<hidN;++j)System.out.print(bp2.dw2[i][j]+" ");System.out.println();}while(true){double testout[]=new double[outN];double testin[]=new double[attN];Scanner testinScanner=new Scanner(System.in);for(int i=0;i<attN;++i){testin[i]=testinScanner.nextDouble();}testout=bp2.getResault(testin);for(int i=0;i<outN;++i)System.out.print(testout[i]+" ");System.out.println(outN);}} catch (IOException e) {// TODO: handle exception}System.out.println("End");}}class BP2//包含一个隐含层的神经网络{double dw1[][],dw2[][];int hidN;//隐含层单元个数int samN;//学习样例个数int attN;//输入单元个数int outN;//输出单元个数int times;//迭代次数double rate;//学习速率boolean trained=false;//保证在得结果前，先训练BP2(int attN,int outN,int hidN,int samN,int times,double rate) {this.attN=attN;this.outN=outN;this.hidN=hidN;this.samN=samN;dw1=new double[hidN][attN+1];//每行最后一个是阈值w0for(int i=0;i<hidN;++i)//每行代表所有输入到i隐藏单元的权值{for(int j=0;j<=attN;++j)dw1[i][j]=Math.random()/2;}dw2=new double[outN][hidN+1];//输出层权值,每行最后一个是阈值w0 for(int i=0;i<outN;++i)//每行代表所有隐藏单元到i输出单元的权值{for(int j=0;j<=hidN;++j)dw2[i][j]=Math.random()/2;}this.times=times;this.rate=rate;}public void train(double samin[][],double samout[][]){double dis=0;//总体误差int count=times;double temphid[]=new double[hidN];double tempout[]=new double[outN];double wcout[]=new double[outN];double wchid[]=new double[hidN];while((count--)>0)//迭代训练{dis=0;for(int i=0;i<samN;++i)//遍历每个样例samin[i]{for(int j=0;j<hidN;++j)//计算每个隐含层单元的结果{temphid[j]=0;for(int k=0;k<attN;++k)temphid[j]+=dw1[j][k]*samin[i][k];temphid[j]+=dw1[j][attN];//计算阈值产生的隐含层结果temphid[j]=1.0/(1+Math.exp(-temphid[j] ));}for(int j=0;j<outN;++j)//计算每个输出层单元的结果{tempout[j]=0;for(int k=0;k<hidN;++k)tempout[j]+=dw2[j][k]*temphid[k];tempout[j]+=dw2[j][hidN];//计算阈值产生的输出结果tempout[j]=1.0/(1+Math.exp( -tempout[j] ));}//计算每个输出单元的误差项for(int j=0;j<outN;++j){wcout[j]=tempout[j]*(1-tempout[j])*(samout[i][j]-tempout[j]);dis+=Math.pow((samout[i][j]-tempout[j]),2);}//计算每个隐藏单元的误差项for(int j=0;j<hidN;++j){double wche=0;for(int k=0;k<outN;++k)//计算输出项误差和{wche+=wcout[k]*dw2[k][j];}wchid[j]=temphid[j]*(1-temphid[j])*wche;}//改变输出层的权值for(int j=0;j<outN;++j){for(int k=0;k<hidN;++k){dw2[j][k]+=rate*wcout[j]*temphid[k];}dw2[j][hidN]=rate*wcout[j];}//改变隐含层的权值for(int j=0;j<hidN;++j){for(int k=0;k<attN;++k){dw1[j][k]+=rate*wchid[j]*samin[i][k];}dw1[j][attN]=rate*wchid[j];}}if(dis<0.003)break;}trained=true;}public double[] getResault(double samin[]){double temphid[]=new double[hidN];double tempout[]=new double[outN];if(trained==false)return null;for(int j=0;j<hidN;++j)//计算每个隐含层单元的结果{temphid[j]=0;for(int k=0;k<attN;++k)temphid[j]+=dw1[j][k]*samin[k];temphid[j]+=dw1[j][attN];//计算阈值产生的隐含层结果temphid[j]=1.0/(1+Math.exp(-temphid[j] ));}for(int j=0;j<outN;++j)//计算每个输出层单元的结果{tempout[j]=0;for(int k=0;k<hidN;++k)tempout[j]+=dw2[j][k]*temphid[k];tempout[j]+=dw2[j][hidN];//计算阈值产生的输出结果tempout[j]=1.0/(1+Math.exp( -tempout[j]));//System.out.print(tempout[j]+" ");}return tempout;}}。