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

#define N_Out 2 //输出向量维数

#define N_In 3 //输入向量维数

#define N_Sample 6 //样本数量

//BP人工神经网络

typedef struct

{

int LayerNum; //中间层数量

double v[N_In][50]; //中间层权矩阵i,中间层节点最大数量为50 double w[50][N_Out]; //输出层权矩阵

double StudyRate; //学习率

double Accuracy; //精度控制参数

int MaxLoop; //最大循环次数

} BPNet;

//Sigmoid函数

double fnet(double net)

{

return 1/(1+exp(-net));

}

//初始化

int InitBpNet(BPNet *BP);

//训练BP网络，样本为x，理想输出为y

int TrainBpNet(BPNet *BP, double x[N_Sample][N_In], int y[N_Sample][N_Out]) ;

//使用BP网络

int UseBpNet(BPNet *BP);

//主函数

int main()

{

//训练样本

double x[N_Sample][N_In] = {

{0.8,0.5,0},

{0.9,0.7,0.3},

{1,0.8,0.5},

{0,0.2,0.3},

{0.2,0.1,1.3},

{0.2,0.7,0.8}};

//理想输出

int y[N_Sample][N_Out] = {

{0,1},

{1,1},

{1,0},

{1,0}};

BPNet BP;

InitBpNet(&BP); //初始化BP网络结构

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网络

int UseBpNet(BPNet *BP)

{

double Input[N_In];

double Out1[50];

double Out2[N_Out]; //Out1为中间层输出,Out2为输出层输出

//持续执行，除非中断程序

while (1)

{

printf("请输入3个数：\n");

int i, j;

for (i = 0; i < N_In; i++)

scanf_s("%f", &Input[i]);

double Tmp;

for (i = 0; i < (*BP).LayerNum; i++)

{

Tmp = 0;

for (j = 0; j < N_In; j++)

Tmp += Input[j] * (*BP).v[j][i];

Out1[i] = fnet(Tmp);

}

for (i = 0; i < N_Out; i++)

{

Tmp = 0;

for (j = 0; j < (*BP).LayerNum; j++)

Tmp += Out1[j] * (*BP).w[j][i];

Out2[i] = fnet(Tmp);

}

printf("结果：");

for (i = 0; i < N_Out; i++)

printf("%.3f ", Out2[i]);

printf("\n");

}

return 1;

}

//训练BP网络，样本为x，理想输出为y

int TrainBpNet(BPNet *BP, double x[N_Sample][N_In], int y[N_Sample][N_Out]) {

double f = (*BP).Accuracy; //精度控制参数

double a = (*BP).StudyRate; //学习率

int LayerNum = (*BP).LayerNum; //中间层节点数

double v[N_In][50], w[50][N_Out]; //权矩阵

double ChgH[50], ChgO[N_Out]; //修改量矩阵

double Out1[50], Out2[N_Out]; //中间层和输出层输出量

int MaxLoop = (*BP).MaxLoop; //最大循环次数

int i, j, k, n;

double Tmp;

for (i = 0; i < N_In; i++)// 复制结构体中的权矩阵

for (j = 0; j < LayerNum; j++)

v[i][j] = (*BP).v[i][j];

for (i = 0; i < LayerNum; i++)

for (j = 0; j < N_Out; j++)

w[i][j] = (*BP).w[i][j];

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 < N_In; j++)

Tmp = Tmp + x[i][j] * v[j][k];

Out1[k] = 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 + Out1[j] * w[j][k];

Out2[k] = fnet(Tmp);

}

//计算输出层的权修改量

for (j = 0; j < N_Out; j++)

ChgO[j] = Out2[j] * (1 - Out2[j]) * (y[i][j] - Out2[j]);

//计算输出误差

for (j = 0; j < N_Out ; j++)

e = e + (y[i][j] - Out2[j]) * (y[i][j] - Out2[j]);

//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 + w[j][k] * ChgO[k];

ChgH[j] = Tmp * Out1[j] * (1 - Out1[j]);

}

//修改输出层权矩阵

for (j = 0; j < LayerNum; j++)

for (k = 0; k < N_Out; k++)

w[j][k] = w[j][k] + a * Out1[j] * ChgO[k];

for (j = 0; j < N_In; j++)

for (k = 0; k < LayerNum; k++)

v[j][k] = v[j][k] + a * x[i][j] * ChgH[k];

}

if (n % 10 == 0)

printf("误差: %f\n", e);

}

printf("总共循环次数：%d\n", n);

printf("调整后的中间层权矩阵：\n");

for (i = 0; i < N_In; i++)

{

for (j = 0; j < LayerNum; j++)

printf("%f ", v[i][j]);

printf("\n");

}

printf("调整后的输出层权矩阵：\n");

for (i = 0; i < LayerNum; i++) {

for (j = 0; j < N_Out; j++)

printf("%f ", w[i][j]);

printf("\n");

}

//把结果复制回结构体

for (i = 0; i < N_In; i++)

for (j = 0; j < LayerNum; j++)

(*BP).v[i][j] = v[i][j];

for (i = 0; i < LayerNum; i++)

for (j = 0; j < N_Out; j++)

(*BP).w[i][j] = w[i][j];

printf("BP网络训练结束！\n");

return 1;

}

//初始化

int InitBpNet(BPNet *BP)

{

printf("请输入中间层节点数，最大数为100：\n");

scanf_s("%d", &(*BP).LayerNum);

printf("请输入学习率：\n");

scanf_s("%lf", &(*BP).StudyRate); //(*BP).StudyRate为double型数据，所以必须是lf

printf("请输入精度控制参数：\n");

scanf_s("%lf", &(*BP).Accuracy);

printf("请输入最大循环次数：\n");

scanf_s("%d", &(*BP).MaxLoop);

int i, j;

srand((unsigned)time(NULL));

for (i = 0; i < N_In; i++)

for (j = 0; j < (*BP).LayerNum; j++)

(*BP).v[i][j] = rand() / (double)(RAND_MAX);

for (i = 0; i < (*BP).LayerNum; i++)

for (j = 0; j < N_Out; j++)

(*BP).w[i][j] = rand() / (double)(RAND_MAX);

return 1;

}