BP神经网络算法的C语言实现代码

1、BP神经网络算法的C语言实现代码/BP神经网络算法,c语言版本/VS2010下，无语法错误，可直接运行添加了简单注释欢迎学习交流#include <stdlib.LayerNum>#include <math.LayerNum>#include <stdio.LayerNum>#include <time.LayerNum>#define N_Out 2输出向量维数#define N_In 3 隔入向量维数#define N_Sample 6 样本数量/BP人工神经网络typedef struct(int Lay er Num;中间层数量doub

2、le vN_In50; 中间层权矩阵i,中间层节点最大数量为50double w50N_Out;输出层权矩阵double Study Rate;学习率double Accuracy;int MaxLoop;精度控制参数最大循环次数 BPNet;/Sigmoid 函数double fnet(double net)(return l/(l+exp(-net);) 初始化int InitBpNet(BPNet *BP);/训练BP网络，样本为x,理想输出为yint Tr ainBpN et(BPN et *BP, double xN_Sampleint yN_Sample |N_Out|);使用BP

3、网络int UseBpNet(BPNet *BP);主函数int main()训练样本double xN_SampleN_In = 08,0.5,0, 09,07。3,1,0.8,0.5,0,0.2,0.3, 02,01,1.3, 0.2,0.7,0.8；理想输出int y N_Sample N_Out = 0,1,0,1, 0,1, 1,1, 1,0, 1,0)；初始化BP网络BPNet BP;InitBpNet(&BP);结构TrainBpNet(&BP, x, y); 训练 BP 神经网络UseBpNet(&BP);测试 BP 神经网络return 1;)使用BP

4、网络int UseBpNet(BPNet *BP)(double InputN_In;double Out 1 50;double Out2N_Out; /Outl 为中间层输出Qut2为输出层输出持续执行，除非中断程序while (1)printfC请输入3个数：nn);int ij;for (i = 0; i < N_In; i+)scanf_s(n%r &Inputi);double Tmp;for (i = 0; i < (*BP).LayerNum; i+)|Tmp = 0;for (j = 0; j < N_In; j+)Tmp += Inputs *Ou

5、tli = fnet(Tmp);)for (i = 0; i < N_Out; i+)(Tmp = 0;for (j = 0; j < (*BP).LayerNum; j+)Tmp += Outlj * (*BP).w皿i;Out2i = fnet(Tmp);)printfC 结果： n);for (i = 0; i < N_Out; i+)printf(H%.3f H,Out2i);printf(nnn);)return 1;)训练BP网络，样本为x,理想输出为yintTrainBpN et (BPN et*BP, doublexN_SampleN_In, int yN_S

6、ample N_Out)double f = (*BP).Accuracy;精度学控制参数double a = (BP).StudyRate;习率int LayerNum = (*BP).LayerNum; 中间层节 点数double vN_In50, w50N_Out; 权矩阵double ChgH50, ChgON_Out;/修改量矩阵double Outl50, Out2N_Out; 中间层和输 出层输出量int MaxLoop(*BP).MaxLoop;最大循环次数int n;double Tmp;for (i = 0; i < N_In; i+)/复制结构体中的权 矩阵for

7、(j = 0; j < LayerNum; j+)viU = (*BP).viU；for (i = 0; i < LayerNum; i+)for (j = 0; j < N_Out; j+)Wij = (*BP).wij;double e = f + 1;对每个样本训练网络for (n = 0; e > f && n < MaxLoop; n+) (e = 0;for (i= 0; i < N_Sample; i+)计算中间层输出向量for (k= 0; k < LayerNum; k+)Tmp = 0;for (j = 0; j &

8、lt; N_In; j+)Tmp = Tmp + xij*vjk;Outlk = fnet(Tmp);)计算输出层输出向量for (k = 0; k < N_Out; k+)Tmp = 0;for (j = 0; j < LayerNum; j+)Tmp = Tmp + Outlj * wjk;Out2k = fnet(Tmp);)计算输出层的权修改量for (j = 0; j < N_Out; j+)ChgOj = Out2j * (1 - Out2j) * (yiU - Out2U)； 计算输出误差 for (j = 0; j < N_Out; j+)e = e +

9、 (yiU - Out2j) * (yiU-Out2UD;计算中间层权修改量for (j = 0; j < LayerNum; j+)Tmp = 0;for (k = 0; k < N_Out; k+)Tmp = Tmp + wjk * ChgOk;ChgHj = Tmp * Outlj * (1 - Outiui)； )修改输出层权矩阵for (j = 0; j < LayerNum; j+)for (k = 0; k < N_Out; k+)wjk = wjk + a * Outlj * ChgOk;for (j = 0; j < N_In; j+)for (

10、k = 0; k < LayerNum; k+) vUk = v皿k + a * xiU * ChgHk;)if (n % 10 = 0)printf("误差：e);)printf("总共循环次数：%dnf n);printf("调整后的中间层权矩阵：nn);for (i = 0; i < N_In; i+)for (j = 0; j < LayerNum; j+) printf(n%f vij);printf(nnn);)printfC调整后的输出层权矩阵：n");for (i = 0; i < LayerNum; i+) fo

11、r (j = 0; j < N_Out; j+) printf(n%f wij);printf(nnn);)把结果复制回结构体for (i = 0; i < N_In; i+)for (j = 0; j < LayerNum; j+) (*BP).vij = vij;for (i = 0; i < LayerNum; i+)for (j = 0; j < N_Out; j+) (*BP).wij = wij;printfBP网络训练结束！ nn);return 1;初始化int InitBpNet(BPNet *BP)printf请输入中间层节点数，最大数为100

12、：W'')；scanf_s(M %d' &(*BP)>LayerNum);printf(1，请输入学习率:n“);scanf_s(n %lf&(BP).StudyRate);/(BP).StudyRate为double型数据，所以必须 是Ifprintf（"请输入精度控制参数：nn）;scanf_s(M %IF &(*BP)>Accuracy);printf请输入最大循环次数：n"）;scanf_s(n%dM, &(*BP).MaxLoop);int ij;srand(unsigned)time(NULL);for (i = 0; i < N_In; i