1、using System;using System.Collections.Generic;using System.Linq;using System.Text;using System;using System.IO;using System.Text;namespace BpANNet/ / BpNet 的摘要說明。/ public class BpNetpublic int inNum;/輸入節(jié)點數(shù)int hideNum;/隱層節(jié)點數(shù)public int outNum;/輸出層節(jié)點數(shù)public int sampleNum;/樣本總數(shù)Random R;double x;/輸入節(jié)點的輸入

2、數(shù)據(jù)double x1;/隱層節(jié)點的輸出double x2;/輸出節(jié)點的輸出double o1;/隱層的輸入double o2;/輸出層的輸入public double , w;/權值矩陣w，這是輸入層與隱藏層之間的權值矩陣public double , v;/權值矩陣V，這是隱藏層與輸出層之間的權值矩陣public double , dw;/權值矩陣wpublic double , dv;/權值矩陣Vpublic double rate;/學習率public double b1;/隱層閾值矩陣public double b2;/輸出層閾值矩陣public double db1;/隱層閾值矩陣

3、public double db2;/輸出層閾值矩陣double pp;/隱藏層的誤差double qq;/輸出層的誤差double yd;/輸出層的教師數(shù)據(jù)，所謂教師數(shù)據(jù)就是實際數(shù)據(jù)而已！public double e;/均方誤差double in_rate;/歸一化比例系數(shù)/用于確定隱藏層的神經(jīng)細胞數(shù)public int computeHideNum(int m,int n)double s=Math.Sqrt(0.43*m*n+0.12*n*n+2.54*m+0.77*n+0.35)+0.51;int ss=Convert.ToInt32(s);return (s-(double)ss)

4、0.5) ? ss+1:ss;public BpNet(double , p,double , t)/ 構造函數(shù)邏輯R=new Random();this.inNum=p.GetLength(1);this.outNum=t.GetLength(1);this.hideNum=computeHideNum(inNum,outNum);/this.hideNum=18;this.sampleNum=p.GetLength(0);Console.WriteLine(輸入節(jié)點數(shù)目： +inNum);Console.WriteLine(隱層節(jié)點數(shù)目：+hideNum);Console.WriteLin

5、e(輸出層節(jié)點數(shù)目：+outNum);Console.ReadLine();/將這些矩陣規(guī)定好矩陣大小x=new doubleinNum;x1=new doublehideNum;x2=new doubleoutNum;o1=new doublehideNum;o2=new doubleoutNum;w = new doubleinNum, hideNum;/權值矩陣w，這是輸入層與隱藏層之間的權值矩陣v=new doublehideNum,outNum;dw=new doubleinNum,hideNum;dv=new doublehideNum,outNum;/閾值b1=new doubl

6、ehideNum;b2=new doubleoutNum;db1=new doublehideNum;db2=new doubleoutNum;/誤差pp = new doublehideNum;/隱藏層的誤差qq = new doubleoutNum;/輸出層的誤差yd = new doubleoutNum;/輸出層的教師數(shù)據(jù)/初始化wfor(int i=0;iinNum;i+)for(int j=0;jhideNum;j+)/NextDouble返回一個介于 0.0 和 1.0 之間的隨機數(shù)。wi,j=(R.NextDouble()*2-1.0)/2;/初始化vfor(int i=0;ih

7、ideNum;i+)for(int j=0;joutNum;j+)vi,j=(R.NextDouble()*2-1.0)/2;rate=0.8;e=0.0;in_rate=1.0;/訓練函數(shù)public void train(double , p,double , t)e=0.0;/求p，t中的最大值double pMax=0.0;/sampleNum為樣本總數(shù)for(int isamp=0;isampsampleNum;isamp+)/inNum是輸入層的節(jié)點數(shù)(即神經(jīng)細胞數(shù))for(int i=0;ipMax)pMax=Math.Abs(pisamp,i);for(int j=0;jpMa

8、x)pMax=Math.Abs(tisamp,j);in_rate=pMax;/end isampfor(int isamp=0;isampsampleNum;isamp+)/數(shù)據(jù)歸一化for(int i=0;iinNum;i+)xi=pisamp,i/in_rate;for(int i=0;ioutNum;i+)ydi=tisamp,i/in_rate;/計算隱層的輸入和輸出for(int j=0;jhideNum;j+)o1j=0.0;for(int i=0;iinNum;i+)o1j+=wi,j*xi;/“權值”*“輸入”的那個累加的過程/這個b1j就是隱藏層的閾值，閾值就是一個輸入為“

9、-1”的累加值x1j=1.0/(1.0+Math.Exp(-o1j-b1j);/計算輸出層的輸入和輸出for(int k=0;koutNum;k+)o2k=0.0;for(int j=0;jhideNum;j+)o2k+=vj,k*x1j;x2k=1.0/(1.0+Math.Exp(-o2k-b2k);/計算輸出層誤差和均方差for(int k=0;koutNum;k+)/ydk是輸出層的教師數(shù)據(jù)，所謂教師數(shù)據(jù)就是實際應該輸出的數(shù)據(jù)而已qqk=(ydk-x2k)*x2k*(1.0-x2k);e+=(ydk-x2k)*(ydk-x2k);/更新V，V矩陣是隱藏層與輸出層之間的權值for(int

10、j=0;jhideNum;j+)vj,k+=rate*qqk*x1j;/計算隱層誤差for(int j=0;jhideNum;j+)/PP矩陣是隱藏層的誤差ppj=0.0;/算法參考我的視頻截圖for(int k=0;koutNum;k+)ppj+=qqk*vj,k;ppj=ppj*x1j*(1-x1j);/更新Wfor(int i=0;iinNum;i+)wi,j+=rate*ppj*xi;/更新b2，輸出層的閾值for(int k=0;koutNum;k+)b2k+=rate*qqk;/更新b1，隱藏層的閾值for(int j=0;jhideNum;j+)b1j+=rate*ppj;/en

11、d isampe=Math.Sqrt(e);/均方差/ adjustWV(w,dw);/ adjustWV(v,dv);/end trainpublic void adjustWV(double , w,double, dw)for(int i=0;iw.GetLength(0);i+)for(int j=0;jw.GetLength(1);j+)wi,j+=dwi,j;public void adjustWV(double w,double dw)for(int i=0;iw.Length;i+)wi+=dwi;/數(shù)據(jù)仿真函數(shù)public double sim(double psim)for

12、(int i=0;iinNum;i+)xi= psimi/in_rate;/in_rate為歸一化系數(shù)for(int j=0;jhideNum;j+)o1j=0.0;for(int i=0;iinNum;i+)o1j=o1j+wi,j*xi;x1j=1.0/(1.0+Math.Exp(-o1j-b1j);for(int k=0;koutNum;k+)o2k=0.0;for(int j=0;jhideNum;j+)o2k=o2k+vj,k*x1j;x2k=1.0/(1.0+Math.Exp(-o2k-b2k);x2k=in_rate*x2k;return x2; /end sim/保存矩陣w,v

13、public void saveMatrix(double , w,string filename)StreamWriter sw=File.CreateText(filename);for(int i=0;iw.GetLength(0);i+)for(int j=0;jw.GetLength(1);j+)sw.Write(wi,j+ );sw.WriteLine();sw.Close();/保存矩陣b1,b2public void saveMatrix(double b,string filename)StreamWriter sw=File.CreateText(filename);for

14、(int i=0;ib.Length;i+)sw.Write(bi+ );sw.Close();/讀取矩陣W,Vpublic void readMatrixW(double , w,string filename)StreamReader sr;trysr = new StreamReader(filename,Encoding.GetEncoding(gb2312);String line;int i=0;while (line = sr.ReadLine() != null)string s1=line.Trim().Split( );for(int j=0;js1.Length;j+)w

15、i,j=Convert.ToDouble(s1j);i+;sr.Close();catch (Exception e)/ Let the user know what went wrong.Console.WriteLine(The file could not be read:);Console.WriteLine(e.Message);/讀取矩陣b1,b2public void readMatrixB(double b,string filename)StreamReader sr;trysr = new StreamReader(filename,Encoding.GetEncoding

16、(gb2312);String line;int i=0;while (line = sr.ReadLine() != null)bi=Convert.ToDouble(line);i+;sr.Close();catch (Exception e)/ Let the user know what went wrong.Console.WriteLine(The file could not be read:);Console.WriteLine(e.Message);/end bpnet /end namespace/主調用程序namespace BpANNet/ / Class1 的摘要說明

17、。/ class Class1/ / 應用程序的主入口點。/ STAThreadstatic void Main(string args)/0.1399,0.1467,0.1567,0.1595,0.1588,0.1622,0.1611,0.1615,0.1685,0.1789,0.1790/ double , p1=new double,0.05,0.02,0.09,0.11,0.12,0.20,0.15,0.22,0.20,0.25,0.75,0.75,0.80,0.83,0.82,0.80,0.90,0.89,0.95,0.89,0.09,0.04,0.1,0.1,0.14,0.21,0

18、.18,0.24,0.22,0.28,0.77,0.78,0.79,0.81,0.84,0.82,0.94,0.93,0.98,0.99;/ double , t1=new double,1,0,1,0,1,0,1,0,1,0,0,1,0,1,0,1,0,1,0,1,1,0,1,0,1,0,1,0,1,0,0,1,0,1,0,1,0,1,0,1;/p1是輸入的信息，一共5組，輸入層為六個節(jié)點,p156double , p1=new double,0.1399,0.1467,0.1567,0.1595,0.1588,0.1622,0.1467,0.1567,0.1595,0.1588,0.162

19、2,0.1611,0.1567,0.1595,0.1588,0.1622,0.1611,0.1615,0.1595,0.1588,0.1622,0.1611,0.1615,0.1685,0.1588,0.1622,0.1611,0.1615,0.1685,0.1789;/t1是輸出信息，一共6組，t161double , t1=new double,0.1622,0.1611,0.1615,0.1685,0.1789,0.1790;BpNet bp=new BpNet(p1,t1);int study=0;dostudy+;bp.train(p1,t1);/ bp.rate=0.95-(0.95-0.3)*study/50000;/ Console.Write(第 + study+次學習： );/ Console.WriteLine( 均方差為 +bp.e);while(bp.e0.001 & study 50000);Console.Write(第 + study+次學習： );Console.WriteLine( 均方差為 +bp.e);bp.saveMatrix(bp.w,w