BP神经网络算法java实现.docx

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 realmagician import 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;isamN;++i) { for(int j=0;jattN;++j) { samin[i][j]=sinScanner.nextDouble(); } for(int j=0;joutN;++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;ihidN;++i) { for(int j=0;jattN;++j) System.out.print(bp2.dw1[i][j]+ ); System.out.println(); } for(int i=0;ioutN;++i) { for(int j=0;jhidN;++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;iattN;++i) { testin[i]=testinScanner.nextDouble(); } testout=bp2.getResault(testin); for(int i=0;ioutN;++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) {