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1、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 FileIn
2、putStream(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 doublesamNattN;double samout=new doubles
3、amNoutN;for(int i=0;isamN;+i)for(int j=0;jattN;+j)saminij=sinScanner.nextDouble();for(int j=0;joutN;+j)samoutij=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;ihidN;+i)for(int j=0;jattN;+j)System.out.print(
4、bp2.dw1ij+ );System.out.println();for(int i=0;ioutN;+i)for(int j=0;jhidN;+j)System.out.print(bp2.dw2ij+ );System.out.println();while(true)double testout=new doubleoutN;double testin=new doubleattN;Scanner testinScanner=new Scanner(System.in);for(int i=0;iattN;+i)testini=testinScanner.nextDouble();te
5、stout=bp2.getResault(testin);for(int i=0;ioutN;+i)System.out.print(testouti+ );System.out.println(outN); catch (IOException e) / TODO: handle exceptionSystem.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 r
6、ate;/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 doublehidNattN+1;/每行最后一个是阈值w0for(int i=0;ihidN;+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)dw1ij=Math.random()/2;dw2=new doubleo
7、utNhidN+1;/输出层权值,每行最后一个是阈值w0for(int i=0;ioutN;+i)/每行代表所有隐藏单元到i输出单元的权值for(int j=0;j0)/迭代训练dis=0;for(int i=0;isamN;+i)/遍历每个样例 saminifor(int j=0;jhidN;+j)/计算每个隐含层单元的结果temphidj=0;for(int k=0;kattN;+k)temphidj+=dw1jk*saminik;temphidj+=dw1jattN;/计算阈值产生的隐含层结果temphidj=1.0/(1+Math.exp(-temphidj );for(int j=0
8、;joutN;+j)/计算每个输出层单元的结果tempoutj=0;for(int k=0;khidN;+k)tempoutj+=dw2jk*temphidk;tempoutj+=dw2jhidN;/计算阈值产生的输出结果tempoutj=1.0/(1+Math.exp( -tempoutj );/计算每个输出单元的误差项for(int j=0;joutN;+j)wcoutj=tempoutj*(1-tempoutj)*(samoutij-tempoutj);dis+=Math.pow(samoutij-tempoutj),2);/计算每个隐藏单元的误差项for(int j=0;jhidN;+
9、j)double wche=0;for(int k=0;koutN;+k)/计算输出项误差和wche+=wcoutk*dw2kj;wchidj=temphidj*(1-temphidj)*wche;/改变输出层的权值for(int j=0;joutN;+j)for(int k=0;khidN;+k)dw2jk+=rate*wcoutj*temphidk;dw2jhidN=rate*wcoutj;/改变隐含层的权值for(int j=0;jhidN;+j)for(int k=0;kattN;+k)dw1jk+=rate*wchidj*saminik;dw1jattN=rate*wchidj;if
10、(dis0.003)break;trained=true;public double getResault(double samin)double temphid=new doublehidN;double tempout=new doubleoutN;if(trained=false)return null;for(int j=0;jhidN;+j)/计算每个隐含层单元的结果temphidj=0;for(int k=0;kattN;+k)temphidj+=dw1jk*samink;temphidj+=dw1jattN;/计算阈值产生的隐含层结果temphidj=1.0/(1+Math.exp(-temphidj );for(int j=0;joutN;+j)/计算每个输出层单元的结果tempoutj=0;for(int k=0;khidN;+k)tempoutj+=dw2jk*temphidk;tempoutj+=dw2jhidN;/计算阈值产生的输出结果tempoutj=1.0/(1+Math.exp( -tempoutj);/System.out.print(tempoutj+ );return tempout;
