bp神经网络详细步骤C实现

1、usingSystem;using；usingSystem.Linq;usingSystem.Text;usingSystem;usingSystem.IO;usingSystem.Text;namespaceBpANNet/<summary> /BpNet的摘要说明. /</summary> publicclassBpNetpublicintinNum;/ 输入节点数inthideNum;隐层节点数publicintoutNum;/输出层节点数publicintsampleNum;/ 样本总数RandomR;doublex;输入节点的输入数据doublex1;/隐层节

2、点的输出doublex2;/输出节点的输出doubleo1;/隐层的输入doubleo2;/输出层的输入publicdouble,w;/权值矩阵w,这是输入层与隐藏层之间的权值矩阵publicdouble,v;/权值矩阵V,这是隐藏层与输出层之间的权值矩阵publicdouble,dw;/ 权值矩阵 wpublicdouble,dv;/ 权值矩阵 Vpublicdoublerate;/ 学习率publicdoubleb1;/ 隐层阈值矩阵publicdoubleb2;/输出层阈值矩阵publicdoubledb1;/ 隐层阈值矩阵publicdoubledb2;/输出层阈值矩阵doublepp

3、;/隐藏层的误差doubleqq;/输出层的误差doubleyd;输出层的教师数据，所谓教师数据就是实际数据而B! |publicdoublee;/ 均方误差doublein_rate;/归一化比例系数用于确定隐藏层的神经细胞数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

4、,t)构造函数逻辑R=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("输入节点数目："+inNum);Console.WriteLine("隐层节点数目："+hideNum);Console.WriteLine("输出层节点数目:"+outN

5、um);Console.ReadLine();将这些矩阵规定好矩阵大小x=newdoubleinNum;x1=newdoublehideNum;x2=newdoubleoutNum;o1=newdoublehideNum;o2=newdoubleoutNum;w=newdoubleinNum,hideNum;权值矩阵 w,这是输入层与隐藏层之间的权值矩阵v=newdoublehideNum,outNum;dw=newdoubleinNum,hideNum;dv=newdoublehideNum,outNum;阈值b1=newdoublehideNum;b2=newdoubleoutNum;db

6、1=newdoublehideNum;db2=newdoubleoutNum;误差pp=newdoublehideNum; 隐藏层的误差qq=newdoubleoutNum; 输出层的误差yd=newdoubleoutNum;输出层的教师数据初始化wfor(inti=0;i<inNum;i+)for(intj=0;j<hideNum;j+)/NextDouble返回一个介于0.0和1.0之间的随机数。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=

7、(R.NextDouble()*2-1.0)/2;rate=0.8;e=0.0;in_rate=1.0;?训练函数publicvoidtrain(double,p,double,t)e=0.0;/求p， t 中的最大值doublepMax=0.0;/sampleNum 为样本总数for(intisamp=0;isamp<sampleNum;isamp+)/inNum 是输入层的节点数(即神经细胞数)for(inti=0;i<inNum;i+)if(Math.Abs(pisamp,i)>pMax)pMax=Math.Abs(pisamp,i);for(intj=0;j<o

8、utNum;j+)if(Math.Abs(tisamp,j)>pMax)pMax=Math.Abs(tisamp,j);in_rate=pMax;/endisamp for(intisamp=0;isamp<sampleNum;isamp+) /数据归一化for(inti=0;i<inNum;i+)xi=pisamp,i/in_rate;for(inti=0;i<outNum;i+)ydi=tisamp,i/in_rate;/计算隐层的输入和输出for(intj=0;j<hideNum;j+)o1j=0.0;for(inti=0;i<inNum;i+)o1j

9、+=wi,j*xi;/ 权值 ” *输入 “”的那个累加的过程这个b1j就是隐藏层的阈值，阈值就是一个输入为-1”的累加值x1j=1.0/(1.0+Math.Exp(-o1j-b1j);/计算输出层的输入和输出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);/计算输出层误差和均方差for(intk=0;k<outNum;k+)/ydk 是输出层的教师数据，所谓教师数据就是实际应该输出的数据而已qqk=(ydk-x2k)*x2

10、k*(1.0-x2k);e+=(ydk-x2k)*(ydk-x2k);/更新V， V 矩阵是隐藏层与输出层之间的权值for(intj=0;j<hideNum;j+)vj,k+=rate*qqk*x1j;/计算隐层误差for(intj=0;j<hideNum;j+)/PP 矩阵是隐藏层的误差ppj=0.0;/算法参考我的视频截图for(intk=0;k<outNum;k+)ppj+=qqk*vjMppj=ppj*x1j*(1-x1j)；更新Wfor(inti=0;i<inNum;i+)wi,j+=rate*ppj*xi;更新b2,输出层的阈值for(intk=0;k<

11、;outNum;k+)b2k+=rate*qqk;更新bl ,隐藏层的阈值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;数据仿真函数publicdoublesim(doublepsim)for(inti=0;i<inNum;i+)xi=psimi/in_rate;/in_rate为归一化系数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保存矩阵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();保存矩阵b1

14、,b2publicvoidsaveMatrix(doubleb,stringfilename)StreamWritersw=File.CreateText(filename);for(inti=0;i<b.Length;i+)sw.Write(bi+"");sw.Close();读取矩阵 W,VpublicvoidreadMatrixW(double,w,stringfilename)StreamReadersr;try?sr=newStreamReader(filename,Encoding.GetEncoding("gb23 12");?Str

15、ingline;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);读取矩阵b1,b2publicvoidreadMat

16、rixB(doubleb,stringfilename)StreamReadersr;try?sr=newStreamReader(filename,Encoding.GetEncoding("gb23 12");?Stringline;inti=0;?while(line=sr.ReadLine()!=null)?bi=Convert.ToDouble(line);i+;sr.Close();catch(Exceptione)?/Lettheuserknowwhatwentwrong.Console.WriteLine("Thefilecouldnotberea

17、d:");Console.WriteLine(e.Message);?endbpnetendnamespace主调用程序namespaceBpANNet/<summary>/Classi的摘要说明。/</summary>classClassl/<summary>/应用程序的主入口点。/</summary>STAThreadstaticvoidMain(stringargs)0.1399,0.1467,0.1567,0.1595,0.1588,0.1622,0.1611,0.1615,0.1685,0.1789,0.1790double,

18、p1=newdouble,0.05,0.02,0.09,0.11,0.12,0.20, 0.15,0.22,0.20,0.25,0.75,0.75,0.80,0.83,0.82,0.80,0.9 0,0.89,0.95,0.89,0.09,0.04,0.1,0.1,0.14,0.21,0.18,0.24, 0.22,0.28,0.77,0.78,0.79,0.81,0.84,0.82,0.94,0.93,0.9 8,0.99；double,t1=newdouble,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,

19、0,1,0,1, 0,1,0,1；/p1是输入的信息，一共5组，输入层为六个节点,p156 double,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, 0.1588,0.1622,0.1611,0.1615,0.1685,0.1789;/t1是输出信息，一共 6组,t161

20、double,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+"次学习："); Console.WriteLine("均方差为"+bp.e);while(bp.e>0.001&&study<50000);Console.Write("第"+study+"次学习：")； Console.WriteLine("均方差为"+bp.e); bp.saveMatrix(bp.w,"w.txt");bp.saveMatrix(bp