bp神經(jīng)網(wǎng)絡(luò)詳細(xì)步驟C#實(shí)現(xiàn)

1、usingSystem;using;usingSystem.Linq;usingSystem.Text;usingSystem;usingSystem.IO;usingSystem.Text;namespaceBpANNet(/<summary>/BpNet的摘要說(shuō)明./</summary>publicclassBpNetpublicintinNum;/輸入節(jié)點(diǎn)數(shù)inthideNum;/隱層節(jié)點(diǎn)數(shù)publicintoutNum;/輸出層節(jié)點(diǎn)數(shù)publicintsampleNum;/樣本總數(shù)RandomR;doublex;/輸入節(jié)點(diǎn)的輸入數(shù)據(jù)doublex1;/隱層節(jié)點(diǎn)的

2、輸出doublex2;/輸出節(jié)點(diǎn)的輸出doubleo1;/隱層的輸入doubleo2;/輸出層的輸入publicdouble,w;/權(quán)值矩陣w,這是輸入層與隱藏層之間的權(quán)值矩陣publicdouble,v;/權(quán)值矩陣V,這是隱藏層與輸出層之間的權(quán)值矩陣publicdouble,dw;/權(quán)值矩陣wpublicdouble,dv;/權(quán)值矩陣Vpublicdoublerate;/學(xué)習(xí)率publicdoubleb1;/隱層閾值矩陣publicdoubleb2;/輸出層閾值矩陣publicdoubledb1;/隱層閾值矩陣publicdoubledb2;/輸出層閾值矩陣doublepp;/隱藏層的誤差d

3、oubleqq;/輸出層的誤差doubleyd;輸出層的教師數(shù)據(jù)，所謂教師數(shù)據(jù)就是實(shí)際數(shù)據(jù)而B(niǎo)!|publicdoublee;/均方誤差doublein_rate;/歸一化比例系數(shù)用于確定隱藏層的神經(jīng)細(xì)胞數(shù)publicintcomputeHideNum(intm,intn)(doubles=Math.Sqrt(0.43*m*n+0.12*n*n+2.54*m+0.77*n+0.35)+0.51;intss=Convert.ToInt32(s);return(s-(double)ss)>0.5)?ss+1:ss;publicBpNet(double,p,double,t)(構(gòu)造函數(shù)邏輯R=

4、newRandom();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(diǎn)數(shù)目："+inNum);Console.WriteLine("隱層節(jié)點(diǎn)數(shù)目："+hideNum);Console.WriteLine("輸出層節(jié)點(diǎn)數(shù)目:"+outNum);Console

5、.ReadLine();將這些矩陣規(guī)定好矩陣大小x=newdoubleinNum;x1=newdoublehideNum;x2=newdoubleoutNum;o1=newdoublehideNum;o2=newdoubleoutNum;w=newdoubleinNum,hideNum;/權(quán)值矩陣w,這是輸入層與隱藏層之間的權(quán)值矩陣v=newdoublehideNum,outNum;dw=newdoubleinNum,hideNum;dv=newdoublehideNum,outNum;閾值b1=newdoublehideNum;b2=newdoubleoutNum;db1=newdouble

6、hideNum;db2=newdoubleoutNum;誤差pp=newdoublehideNum;/隱藏層的誤差qq=newdoubleoutNum;/輸出層的誤差yd=newdoubleoutNum;/輸出層的教師數(shù)據(jù)初始化wfor(inti=0;i<inNum;i+)for(intj=0;j<hideNum;j+)/NextDouble返回一個(gè)介于0.0和1.0之間的隨機(jī)數(shù)。wi,j=(R.NextDouble()*2-1.0)/2;)初始化vfor(inti=0;i<hideNum;i+)for(intj=0;j<outNum;j+)vi,j=(R.NextDo

7、uble()*2-1.0)/2;)rate=0.8;e=0.0;in_rate=1.0;?)訓(xùn)練函數(shù)publicvoidtrain(double,p,double,t)e=0.0;求p,t中的最大值doublepMax=0.0;/sampleNum為樣本總數(shù)for(intisamp=0;isamp<sampleNum;isamp+)(/inNum是輸入層的節(jié)點(diǎn)數(shù)(即神經(jīng)細(xì)胞數(shù))for(inti=0;i<inNum;i+)(if(Math.Abs(pisamp,i)>pMax)(pMax=Math.Abs(pisamp,i);for(intj=0;j<outNum;j+)

8、(if(Math.Abs(tisamp,j)>pMax)(pMax=Math.Abs(tisamp,j);in_rate=pMax;/endisampfor(intisamp=0;isamp<sampleNum;isamp+)(數(shù)據(jù)歸一化for(inti=0;i<inNum;i+)(xi=pisamp,i/in_rate;for(inti=0;i<outNum;i+)(ydi=tisamp,i/in_rate;計(jì)算隱層的輸入和輸出for(intj=0;j<hideNum;j+)(o1j=0.0;for(inti=0;i<inNum;i+)(o1j+=wi,j

9、*xi;/權(quán)值”輸入”的那個(gè)累加的過(guò)程)這個(gè)b1j就是隱藏層的閾值，閾值就是一個(gè)輸入為-1”的累加值x1j=1.0/(1.0+Math.Exp(-o1j-b1j);)計(jì)算輸出層的輸入和輸出for(intk=0;k<outNum;k+)(o2k=0.0;for(intj=0;j<hideNum;j+)(o2k+=vj,k*x1j;)x2k=1.0/(1.0+Math.Exp(-o2k-b2k);)計(jì)算輸出層誤差和均方差for(intk=0;k<outNum;k+)(/ydk是輸出層的教師數(shù)據(jù)，所謂教師數(shù)據(jù)就是實(shí)際應(yīng)該輸出的數(shù)據(jù)而已qqk=(ydk-x2k)*x2k*(1.0-x

10、2k);e+=(ydk-x2k)*(ydk-x2k);更新V,V矩陣是隱藏層與輸出層之間的權(quán)值for(intj=0;j<hideNum;j+)(vj,k+=rate*qqk*x1j;計(jì)算隱層誤差for(intj=0;j<hideNum;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(intk=0;k<outNum;k+)(ppj+=qqk*vj,k；)ppj=ppj*x1j*(1-x1j);更新Wfor(inti=0;i<inNum;i+)(wi,j+=rate*ppj*xi;)更新b2,輸出層的閾值for(intk=0;k<outNu

11、m;k+)(b2k+=rate*qqk;)更新b1,隱藏層的閾值for(intj=0;j<hideNum;j+)(b1j+=rate*ppj;)/endisampe=Math.Sqrt(e);/均方差/adjustWV(w,dw);/adjustWV(v,dv);/endtrainpublicvoidadjustWV(double,w,double,dw)for(inti=0;i<w.GetLength(0);i+)for(intj=0;j<w.GetLength(1);j+)wi,j+=dwi,j;publicvoidadjustWV(doublew,doubledw)(f

12、or(inti=0;i<w.Length;i+)(wi+=dwi;數(shù)據(jù)仿真函數(shù)publicdoublesim(doublepsim)(for(inti=0;i<inNum;i+)xi=psimi/in_rate;/in_rate為歸一化系數(shù)for(intj=0;j<hideNum;j+)(o1j=0.0;for(inti=0;i<inNum;i+)o1j=o1j+wi,j*xi;x1j=1.0/(1.0+Math.Exp(-o1j-b1j);)for(intk=0;k<outNum;k+)o2k=0.0;for(intj=0;j<hideNum;j+)o2k

13、=o2k+vj,k*x1j;x2k=1.0/(1.0+Math.Exp(-o2k-b2k);x2k=in_rate*x2k;)?returnx2;/endsim保存矩陣w,vpublicvoidsaveMatrix(double,w,stringfilename)StreamWritersw=File.CreateText(filename);for(inti=0;i<w.GetLength(0);i+)(for(intj=0;j<w.GetLength(1);j+)(sw.Write(wi,j+"");)sw.WriteLine();)sw.Close();)

14、保存矩陣b1,b2publicvoidsaveMatrix(doubleb,stringfilename)(StreamWritersw=File.CreateText(filename);for(inti=0;i<b.Length;i+)(sw.Write(bi+"");)sw.Close();)讀取矩陣W,VpublicvoidreadMatrixW(double,w,stringfilename)(StreamReadersr;try?(sr=newStreamReader(filename,Encoding.GetEncoding("gb2312&q

15、uot;);?Stringline;inti=0;while(line=sr.ReadLine()!=null)?(strings1=line.Trim().Split('');for(intj=0;j<s1.Length;j+)(wi,j=Convert.ToDouble(s1j);)i+；)sr.Close();)catch(Exceptione)?(/Lettheuserknowwhatwentwrong.Console.WriteLine("Thefilecouldnotberead:");Console.WriteLine(e.Message

16、);)讀取矩陣b1,b2publicvoidreadMatrixB(doubleb,stringfilename)(StreamReadersr;try?sr=newStreamReader(filename,Encoding.GetEncoding("gb2312");?Stringline;inti=0;?while(line=sr.ReadLine()!=null)?bi=Convert.ToDouble(line);i+;sr.Close();catch(Exceptione)?/Lettheuserknowwhatwentwrong.Console.WriteLi

17、ne("Thefilecouldnotberead:");Console.WriteLine(e.Message);?/endbpnet/endnamespace主調(diào)用程序namespaceBpANNet(/<summary>/Classi的摘要說(shuō)明。/</summary>classClassl(/<summary>/應(yīng)用程序的主入口點(diǎn)。/</summary>STAThreadstaticvoidMain(stringargs)(/0.1399,0.1467,0.1567,0.1595,0.1588,0.1622,0.1611

18、,0.1615,0.1685,0.1789,0.1790/double,p1=newdouble,0.05,0.02,0.09,0.11,0.12,0.20,0.15,0.22,0.20,0.25,0.75,0.75,0.80,0.83,0.82,0.80,0.90,0.89,0.95,0.89,0.09,0.04,0.1,0.1,0.14,0.21,0.18,0.24,0.22,0.28,0.77,0.78,0.79,0.81,0.84,0.82,0.94,0.93,0.98,0.99;/double,t1=newdouble,1,0,1,0,1,0,1,0,1,0,0,1,0,1,0,1,

19、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(gè)節(jié)點(diǎn),p156double,p1=newdouble,0.1399,0.1467,0.1567,0.1595,0.1588,0.1622,0.1467,0.1567,0.1595,0.1588,0.1622,0.1611,0.1567,0.1595,0.1588,0.1622,0.1611,0.1615,0.1595,0.1588,0.1622,0.1611,0.1615,0.1685,0.1588,0.1622,0.1611,0.1615,0.1685,0

20、.1789;/t1是輸出信息，一共6組,t161double,t1=newdouble,0.1622,0.1611,0.1615,0.1685,0.1789,0.1790;BpNetbp=newBpNet(p1,t1);intstudy=0;dostudy+;bp.train(p1,t1);bp.rate=0.95-(0.95-0.3)*study/50000;/Console.Write("第"+study+"次學(xué)習(xí)：");Console.WriteLine("均方差為"+bp.e);while(bp.e>0.001&&study<50000);Console.Write("第"+study+"次學(xué)習(xí)：");Console.WriteLine("均方差為"+bp.e);bp.saveMatrix(bp.w,"w.txt");bp.saveMatri