bp神经网络详细步骤C#实现Word下载.docx

1、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;publicdoubledb2;doublepp;/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已！

2、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,t）/构造函数逻辑R=newRandom（）;this.inNum=p.GetLength（1）;this.outNum=t.G

3、etLength（1）;this.hideNum=computeHideNum（inNum,outNum）;/this.hideNum=18;this.sampleNum=p.GetLength（0）;Console.WriteLine（输入节点数目：+inNum）;隐层节点数目：+hideNum）;输出层节点数目：+outNum）;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=new

4、doubleinNum,hideNum;v=newdoublehideNum,outNum;dw=newdoubleinNum,hideNum;dv=newdoublehideNum,outNum;/阈值b1=newdoublehideNum;b2=newdoubleoutNum;db1=newdoublehideNum;db2=newdoubleoutNum;/误差pp=newdoublehideNum;qq=newdoubleoutNum;yd=newdoubleoutNum;/输出层的教师数据/初始化wfor（inti=0;iinNum;i+）for（intj=0;jhideNum;j+

5、）/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voutNum;vi,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）/求p，t中的最大值doublepMax=0.0;/sampleNum为样本总数for（intisamp=0;isamppMax）pMax=Math.Abs（pisamp,i）;if（Math.Abs（tisamp,j）pMax=Math.Abs（tisam

6、p,j）;in_rate=pMax;/endisamp/数据归一化xi=pisamp,i/in_rate;ydi=tisamp,i/in_rate;/计算隐层的输入和输出o1j=0.0;o1j+=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;k0.001&study50000）;Console.Write（bp.saveMatrix（bp.w,w.txtbp.saveMatrix（bp.v,v.txtbp.saveM

7、atrix（bp.b1,b1.txtbp.saveMatrix（bp.b2,b2.txt/double,p2=newdouble,0.05,0.02,0.09,0.11,0.12,0.20,0.15,0.22,0.20,0.25,0.75,0.75,0.80,0.83,0.82,0.80,0.90,0.89,0.95,0.89,0.09,0.04,0.1,0.1,0.14,0.21,0.18,0.24,0.22,0.28,0.77,0.78,0.79,0.81,0.84,0.82,0.94,0.93,0.98,0.99;double,p2=newdouble,0.1622,0.1611,0.1615,0.1685,0.1789,0.1790;intaa=bp.inNum;intbb=bp.outNum;intcc=p2.GetLength（0）;doublep21=newdoubleaa;doublet2=newdoublebb;for（intn=0;ncc;n+）aa;p21i=p2n,i;t2=bp.sim（p21）;t2.Length;Console.WriteLine（t2i+