bp神经网络详细步骤C实现.docx
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bp神经网络详细步骤C实现.docx
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bp神经网络详细步骤C实现
usingSystem;
using;
usingSystem.Linq;
usingSystem.Text;
usingSystem;
usingSystem.10;
usingSystem.Text;
namespaceBpANNet
{
〃/
///BpNet的摘要说明'
///
publicclassBpNet
publicintinNum;〃输入节点数
inthideNum;//隐层节点数
publicintoutNum;//输出层节点数
publicintsampleNum;//样本总数
RandomR;
double[]x;〃输入节点的输入数据
double[]x1;〃隐层节点的输出
double[]x2;〃输出节点的输出
double[]o1;//隐层的输入
double[]o2;//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w
publicdouble[,]dv;〃权值矩阵Vpublicdoublerate;//学习率publicdouble[]b1;〃隐层阈值矩阵publicdouble[]b2;〃输出层阈值矩阵publicdouble[]db1;〃隐层阈值矩阵publicdouble[]db2;〃输出层阈值矩阵double[]pp;〃隐藏层的误差double[]qq;〃输出层的误差
double[]yd;〃输出层的教师数据,所谓教师数据就是实际数据而
已!
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.Tolnt32(s);
return((s-(double)ss)>0.5)?
ss+1:
ss;
}
publicBpNet(double[,]p,double[,]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(O);
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:
"+outNum);
Console.ReadLine();
//将这些矩阵规定好矩阵大小
x=newdouble[inNum];
x1=newdouble[hideNum];
x2=newdouble[outNum];
o1=newdouble[hideNum];
o2=newdouble[outNum];
w=newdouble[inNum,hideNum];〃权值矩阵w,这是输入层与隐
藏层之间的权值矩阵
v=newdouble[hideNum,outNum];
dw=newdouble[inNum,hideNum];
dv=newdouble[hideNum,outNum];
//阈值
b1=newdouble[hideNum];
b2=newdouble[outNum];
db1=newdouble[hideNum];
db2=newdouble[outNum];
//误差
pp=newdouble[hideNum];〃隐藏层的误差
qq=newdouble[outNum];〃输出层的误差
yd=newdouble[outNum];〃输出层的教师数据
//初始化w
for(inti=O;i { for(intj=O;j //NextDouble返回一个介于0.0和1.0之间的随机数。 w[i,j]=(R.NextDouble()*2-1.0)/2; } } 〃初始化v for(inti=0;i { for(intj=0;j { v[i,j]=(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 //inNum是输入层的节点数(即神经细胞数)for(inti=0;i {if(Math.Abs(p[isamp,i])>pMax) {pMax=Math.Abs(p[isamp,i]); } }for(intj=0;j {if(Math.Abs(t[isamp,j])>pMax) {pMax=Math.Abs(t[isamp,j]); } } in_rate=pMax; }//endisampfor(intisamp=0;isamp { //★数据归一化 for(inti=0;i { x[i]=p[isamp,i]/in_rate; } for(inti=0;i { yd[i]=t[isamp,i]/in_rate; } //计算隐层的输入和输出 for(intj=0;j { o1[j]=0.0; for(inti=0;i { o1[j]+=w[i,j]*x[i];//权值”*输“入”的那个累加的过程 } -1”的累加 //这个b1[j]就是隐藏层的阈值,阈值就是一个输入为值 x1[j]=1.0/(1.0+Math.Exp(-o1[j]-b1[j])); } //计算输出层的输入和输出for(intk=0;k { o2[k]=0.0; for(intj=0;j { o2[k]+=v[j,k]*x1[j]; }x2[k]=1.0/(1.0+Math.Exp(-o2[k]-b2[k])); } //计算输出层误差和均方差 { //yd[k]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 qq[k]=(yd[k]-x2[k])*x2[k]*(1.0-x2[k]); e+=(yd[k]-x2[k])*(yd[k]-x2[k]); //更新V,V矩阵是隐藏层与输出层之间的权值for(intj=0;j { v[j,k]+=rate*qq[k]*x1[j]; } } //计算隐层误差 for(intj=0;j { //PP矩阵是隐藏层的误差 pp[j]=0.0; //算法参考我的视频截图 { pp[j]+=qq[k]*v[j,k]; } PP[j]=PP[j]*x1[j]*(1-x1[j]); 〃更新w for(inti=O;i { w[i,j]+=rate*pp[j]*x[i]; } } 〃更新b2,输出层的阈值 for(intk=0;k { b2[k]+=rate*qq[k]; } 〃更新b1,隐藏层的阈值 { b1[j]+=rate*ppj } }//endisampe=Math.Sqrt(e);〃均方差〃adjustWV(w,dw); //adjustWV(v,dv); }//endtrainpublicvoidadjustWV(double[,]w,double[,]dw) { for(inti=O;i { for(intj=O;j (1);j++){ w[i,j]+=dw[i,j]; } } publicvoidadjustWV(double[]w,double[]dw) { for(inti=O;i { w[i]+=dw[i]; } } //数据仿真函数 publicdouble[]sim(double[]psim) { for(inti=O;i x[i]=psim[i]/in_rate;//in_rate为归一化系数 for(intj=O;j { o1[j]=0.0; for(inti=O;i o1[j]=o1[j]+w[i,j]*x[i]; x1[j]=1.0/(1.0+Math.Exp(-o1[j]-b1[j])); } for(intk=O;k { o2[k]=0.0; for(intj=O;j o2[k]=o2[k]+v[j,k]*x1[j]; x2[k]=1.0/(1.0+Math.Exp(-o2[k]-b2[k])); x2[k]=in_rate*x2[k]; }? returnx2; }//endsim 〃保存矩阵w,v publicvoidsaveMatrix(double[,]w,stringfilename) { StreamWritersw=File.CreateText(filename); for(inti=O;i { for(intj=O;j (1);j++) { sw.Write(w[i,j]+""); } sw.WriteLine(); } sw.Close(); } 〃保存矩阵b1,b2 publicvoidsaveMatrix(double[]b,stringfilename){ StreamWritersw=File.CreateText(filename); for(inti=O;i { sw.Write(b[i]+""); } sw.Close(); } 〃读取矩阵W,V publicvoidreadMatrixW(double[,]w,stringfilename) { StreamReadersr; try? { sr=newStreamReader(filename,Encoding.GetEncoding("gb23 12"));? Stringline; inti=0; while((line=sr.ReadLine())! =null)? { string[]s1=line.Trim().Split(''); for(intj=O;j { w[i,j]=Convert.ToDouble(s1[j]); } i++; } sr.Close(); } catch(Exceptione)? { //Lettheuserknowwhatwentwrong. Console.WriteLine("Thefilecouldnotberead: "); Console.WriteLine(e.Message); } 〃读取矩阵b1,b2 publicvoidreadMatrixB(double[]b,stringfilename) { StreamReadersr; try? {? sr=newStreamReader(filename,Encoding.GetEncoding("gb23 12"));? Stringline; inti=0;? while((line=sr.ReadLine())! =null)? { b[i]=Convert.ToDouble(line); i++; } sr.Close(); } catch(Exceptione)? { //Lettheuserknowwhatwentwrong. Console.WriteLine("Thefilecouldnotberead: "); Console.WriteLine(e.Message); }? }//endbpnet }//endnamespace//主调用程序namespaceBpANNet { /// ///Classi的摘要说明。 /// classClassl { /// ///应用程序的主入口点。 /// [STAThread] staticvoidMain(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=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[,]t仁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},{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,p1[5][6] 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,t1[6][1] double[,]t1=newdouble[,]{ {0.1622}, {0.1611}, {0.1615}, {0.1685}, {0.1789}, {0.1790}}; BpNetbp=newBpNet(p1,t1); intstudy=0; do { study++; 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.v,"v.txt"); bp.saveMatrix(bp.b1,"b1.txt"); bp.saveMatrix(bp.b2,"b2.txt"); 〃double[,]p2=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[,]p2=newdouble[,]{ {0.1399,0.1467,0.1567,0.1595,0.1588,0.1622}, {0.1622,0.1611,0.1615,0.1685,0.1789,0.1790}}; intaa=bp.inNum; intbb=bp.outNum; intcc=p2.GetLength(0); double[]p21=newdouble[aa]; double[]t2=newdouble[bb]; for(intn=0;n { for(inti=0;i {p21[i]=p2[n,i]; } t2=bp.sim(p21); for(inti=0;i { Console.WriteLine(t2[i]+""); } } Console.ReadLine(); } } }?
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