BP神经网络实例含源码

1、精选优质文档-倾情为你奉上BP神经网络算法实现一：关于BP网络BP (Back Propagation)神经网络，即误差反传误差反向传播算法的学习过程，由信息的正向传播和误差的反向传播两个过程组成。输入层各神经元负责接收来自外界的输入信息，并传递给中间层各神经元；中间层是内部信息处理层，负责信息变换，根据信息变化能力的需求，中间层可以设计为单隐层或者多隐层结构；最后一个隐层传递到输出层各神经元的信息，经进一步处理后，完成一次学习的正向传播处理过程，由输出层向外界输出信息处理结果。当实际输出与期望输出不符时，进入误差的反向传播阶段。误差通过输出层，按误差梯度下降的方式修正各层权值，向隐层、输入层

2、逐层反传。周而复始的信息正向传播和误差反向传播过程，是各层权值不断调整的过程，也是神经网络学习训练的过程，此过程一直进行到网络输出的误差减少到可以接受的程度，或者预先设定的学习次数为止。BP网络主要应用于以下方面：函数逼近、模式识别和分类、数据压缩。BP神经网络有较强的泛化性能，使网络平滑的逼近函数，能合理的响应被训练以外的输入。同时，BP网络又有自己的限制与不足，主要表现在：需要较长的训练时间、网络训练的结果可能使得权值逼近局部最优、训练数据范围外的数据泛化能力较差。为了避免训练陷入局部最优解，本程序采用改进的BP网络训练，既加入动量因子，使得网络在最优解附近有一定的震荡，跳出局部最优的范围

3、。BP网络训练中学习速率与动量因子的选择很重要，在后面的内容中将进行详细的讨论。二：训练的函数程序中训练的函数为一个三输入一输出的非线性函数，如下所示：，网络结构为：351三：程序及相关界面（VB）1 主界面代码：Private Sub Command1_Click()form2.Visible = FalseForm3.Visible = TrueEnd SubPrivate Sub Command2_Click()form2.Visible = FalseForm1.Visible = TrueEnd SubPrivate Sub Command3_Click()form2.Visible

4、 = FalseForm4.Visible = TrueEnd SubPrivate Sub Command4_Click()form2.Visible = FalseForm5.Visible = TrueEnd SubPrivate Sub Command5_Click()EndEnd SubPrivate Sub Form_Load()Command3.Enabled = FalseCommand4.Enabled = FalseEnd Sub2 查看网络结构代码：Private Sub Command1_Click()Form3.Visible = Falseform2.Visible

5、 = TrueEnd Sub3 网络训练 代码：Private Sub Command1_Click()Form1.Visible = Falseform2.Visible = TrueEnd SubPrivate Sub Command2_Click()Label2.Caption = "样本训练中"Dim i As Integer, j As Integer, k As Integer, p As Integer, s As SingleDim Maxx(1 To 3) As Single, Minx(1 To 3) As Single, Meanx(1 To 3) A

6、s SingleDim x(1 To 3, 1 To 100) As Single, sumx(1 To 3) As Single, Temp As SingleDim Datex(1 To 3, 1 To 100) As Single, inputx(1 To 3) As Single, outputx(1 To 100) As SingleDim Ex(1 To 100) As SingleDim time(1 To 5000) As Integer, cishu(1 To 100) As IntegerDim Dv_1(1 To 5, 1 To 3) As Single, Dw_1(1

7、To 5) As SingleDim R As SingleDim Maxd As Single, Mind As SingleDim s1(1 To 5) As Single, y1(1 To 5, 1 To 100) As Single, s2 As Single, y2(1 To 100) As SingleDim deltW(1 To 100) As Single, deltV(1 To 5, 1 To 100) As SingleDim Dw(1 To 5) As Single, Dv(1 To 5, 1 To 3) As SingleDim MyIn(1 To 3) As Sing

8、leDim Errorx(1 To 5000) As SingleRandomizeFor i = 1 To 3 Maxx(i) = 0 Minx(i) = 0 Meanx(i) = 0Next iTemp = 0Maxd = 0Mind = 0For i = 1 To 5 For j = 1 To 3 Dv_1(i, j) = 0 v(i, j) = 2 * Rnd - 1 Next j Dw_1(i) = 0 w(i) = 2 * Rnd - 1Next iFor j = 1 To 3 For i = 1 To 100 x(j, i) = 4 * (2 * Rnd - 1) Next i

9、sumx(j) = 0Next j'求最值For j = 1 To 3 For i = 1 To 100 If x(j, i) >= Maxx(j) Then Maxx(j) = x(j, i) End If If x(j, i) <= Minx(j) Then Minx(j) = x(j, i) Temp = Temp + x(j, i) End If Next i sumx(j) = Temp Temp = 0 Meanx(j) = sumx(j) / 100Next j'归一化For j = 1 To 3 For i = 1 To 100 If Maxx(j)

10、 - x(j, i) >= x(j, i) - Minx(j) Then R = Maxx(j) - x(j, i) Else R = x(j, i) - Minx(j) End If Datex(j, i) = (x(j, i) - Meanx(j) / R Next iNext j'期望输出For i = 1 To 100 For j = 1 To 3 inputx(j) = Datex(j, i) Next j outputx(i) = 2 * (inputx(1) + Sin(inputx(2) + Exp(inputx(3)Next i'输出归一化For i =

11、 1 To 100 If Maxd <= outputx(i) Then Maxd = outputx(i) End If If Mind >= outputx(i) Then Mind = outputx(i) End IfNext iFor i = 1 To 100 Ex(i) = (outputx(i) - Mind) / (Maxd - Mind)Next i'训练For s = 1 To 5000 Step 1 time(s) = s For p = 1 To 100 cishu(p) = p For i = 1 To 3 MyIn(i) = Datex(i, p

12、) Next i For i = 1 To 5 For j = 1 To 3 Temp = Temp + v(i, j) * MyIn(j) Next j s1(i) = Temp Temp = 0 Next i For i = 1 To 5 y1(i, p) = 1 / (1 + Exp(-s1(i) Next i For i = 1 To 3 Temp = y1(i, p) * w(i) + Temp Next i s2 = Temp Temp = 0 y2(p) = 1 / (1 + Exp(-s2) deltW(p) = (Ex(p) - y2(p) * y2(p) * (1 - y2

13、(p) For i = 1 To 5 deltV(i, p) = deltW(p) * w(i) * y1(i, p) * (1 - y1(i, p) Next i Next p'误差 For i = 1 To 100 Temp = Temp + (Ex(i) - y2(i) 2 Next i Errorx(s) = Temp Temp = 0'调整权值 For i = 1 To 5 Dw_1(i) = Dw(i) Next i For i = 1 To 5 For j = 1 To 100 Temp = Temp + deltW(j) * y1(i, j) Next j Dw

14、(i) = Temp Temp = 0 Next i For i = 1 To 5 For j = 1 To 3 Dv_1(i, j) = Dv(i, j) Next j Next i For i = 1 To 5 For j = 1 To 3 For k = 1 To 100 Temp = Temp + deltV(i, k) * Datex(j, k) Next k Dv(i, j) = Temp Temp = 0 Next j Next i For i = 1 To 5 w(i) = 0.2 * Dw(i) + 0.2 * Dw_1(i) + w(i) Next i For i = 1

15、To 3 For j = 1 To 5 v(j, i) = 0.2 * Dv(j, i) + 0.2 * Dv_1(j, i) + v(j, i) Next j Next i'画图 Picture1.Cls Picture1.ScaleTop = 1.5 Picture1.ScaleHeight = -2 Picture1.ScaleLeft = -10 Picture1.ScaleWidth = 120 Picture1.Line (-9, 0)-(110, 0) Picture1.Line (0, 0)-(0, 1.5) For i = 1 To 100 Picture1.PSet

16、 (cishu(i), Ex(i), RGB(128, 128, 0) Picture1.PSet (cishu(i), y2(i), RGB(128, 0, 0) Next i For i = 1 To 99 Picture1.Line (cishu(i), Ex(i)-(cishu(i + 1), Ex(i + 1), RGB(128, 128, 0) Picture1.Line (cishu(i), y2(i)-(cishu(i + 1), y2(i + 1), RGB(128, 0, 0) Next i'延时 For j = 1 To 1000 For k = 1 To 50

17、Next k Next j Picture2.Cls Picture2.Print s DoEventsNext sLabel2.Caption = ""form2.Command3.Enabled = Trueform2.Command4.Enabled = True'泛化Dim test(1 To 3, 1 To 20) As Single, sumE(1 To 3) As SingleDim MaxE(1 To 3) As Single, MinE(1 To 3) As Single, MeanE(1 To 3) As SingleDim MaxxE As S

18、ingle, MinxE As SingleDim des(1 To 3) As Single, outE(1 To 20) As SingleDim MIn(1 To 3) As Single, s11(1 To 5) As Single, y11(1 To 5, 1 To 20) As Single, s22 As SingleDim DateE(1 To 3, 1 To 20) As SingleFor i = 1 To 20 For j = 1 To 3 test(j, i) = 4 * (2 * Rnd - 1) Next jNext iFor j = 1 To 3 For i =

19、1 To 20 If test(j, i) >= MaxE(j) Then MaxE(j) = test(j, i) End If If test(j, i) <= MinE(j) Then MinE(j) = test(j, i) Temp = Temp + test(j, i) End If Next i sumE(j) = Temp Temp = 0 MeanE(j) = sumE(j) / 100Next j'归一化For j = 1 To 3 For i = 1 To 20 If MaxE(j) - test(j, i) >= test(j, i) - Mi

20、nE(j) Then R = MaxE(j) - test(j, i) Else R = test(j, i) - MinE(j) End If DateE(j, i) = (test(j, i) - MeanE(j) / R Next iNext j'求输出 For p = 1 To 20 Ti(p) = p For i = 1 To 3 MIn(i) = DateE(i, p) Next i For i = 1 To 5 For j = 1 To 3 Temp = Temp + v(i, j) * MIn(j) Next j s11(i) = Temp Temp = 0 Next

21、i For i = 1 To 5 y11(i, p) = 1 / (1 + Exp(-s11(i) Next i For i = 1 To 3 Temp = y11(i, p) * w(i) + Temp Next i s22 = Temp Temp = 0 y22(p) = 1 / (1 + Exp(-s22) Next p'输出及归一化For j = 1 To 20 For i = 1 To 3 des(i) = DateE(i, j) Next i outE(j) = 2 * (des(1) + Sin(des(2) + Exp(des(3)Next j'输出归一化For

22、 i = 1 To 20 If MaxxE <= outE(i) Then MaxxE = outE(i) End If If MinxE >= outE(i) Then MinxE = outE(i) End IfNext iFor i = 1 To 20 outD(i) = (outE(i) - MinxE) / (MaxxE - MinxE)Next iEnd Sub4 查看训练结果代码：Private Sub Command1_Click()Form5.Visible = Falseform2.Visible = TrueEnd SubPrivate Sub Command

23、2_Click()Picture1.ClsPicture2.ClsDim i As Integer, j As IntegerFor i = 1 To 5 For j = 1 To 3 Picture2.Print v(i, j); Spc(4); Next j Picture2.Print Picture2.Print Picture1.Print w(i);Next iEnd Sub5 泛化代码：Private Sub Command1_Click()Form4.Visible = Falseform2.Visible = TrueEnd SubPrivate Sub Command2_C

24、lick()For s = 1 To 20 'Picture1.Cls Picture1.ScaleTop = 1.5 Picture1.ScaleHeight = -2 Picture1.ScaleLeft = -5 Picture1.ScaleWidth = 30 Picture1.Line (-5, 0)-(25, 0) Picture1.Line (0, -0.5)-(0, 1.5) For i = 1 To 20 Picture1.PSet (Ti(i), outD(i), RGB(128, 128, 0) Picture1.PSet (Ti(i), y22(i), RGB(128, 0, 0) Next i For i = 1 To 19 Picture1.Line (Ti(i), outD(i)-(Ti(i + 1), outD(i + 1), RGB(128, 128, 0) Picture1.Line (Ti(i), y22(i)-(Ti(i + 1), y22(i + 1), RGB