BP神经网络算法的C语言实现代码.doc

//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 //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},
{0,1},
{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;
}

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