人工神经网络bp算法C语言程序可出图.doc

include
#include
#include
#include
#include
#include
#define OUT_COUT 2 //输出向量维数
#define IN_COUT 3 //输入向量维数
#define COUT 6 //样本数量

typedef struct { //bp人工神经网络结构
int h; //实际使用隐层数量
double v[IN_COUT][50]; //隐藏层权矩阵i,隐层节点最大数量为50
double w[50][OUT_COUT]; //输出层权矩阵
double a; //学习率
double b; //精度控制参数
int LoopCout; //最大循环次数
} bp_nn;
double fnet(double net) { //Sigmoid函数,神经网络激活函数
return 1/(1+exp(-net));
}
int InitBp(bp_nn *bp) { //初始化bp网络
int i,j;
printf("请输入隐层节点数，最大数为100：\n");
scanf("%d", &(*bp).h);
printf("请输入学习率：\n");
scanf("%lf", &(*bp).a); //(*bp).a为double型数据，所以必须是lf
printf("请输入精度控制参数：\n");
scanf("%lf", &(*bp).b);
printf("请输入最大循环次数：\n");
scanf("%d", &(*bp).LoopCout);
srand((unsigned)time(NULL));
for (i = 0; i < IN_COUT; i++)
for (j = 0; j < (*bp).h; j++)
(*bp).v[i][j] = rand() / (double)(RAND_MAX);
for (i = 0; i < (*bp).h; i++)
for (j = 0; j < OUT_COUT; j++)
(*bp).w[i][j] = rand() / (double)(RAND_MAX);
return 1;
}

int TrainBp(bp_nn *bp, float x[COUT][IN_COUT], int y[COUT][OUT_COUT])
{
//训练bp网络，样本为x，理想输出为y
double f = (*bp).b; //精度控制参数
double a = (*bp).a; //学习率
int h = (*bp).h; //隐层节点数
double v[IN_COUT][50], w[50][OUT_COUT]; //权矩阵
double ChgH[50], ChgO[OUT_COUT]; //修改量矩阵
double O1[50], O2[OUT_COUT]; //隐层和输出层输出量
int LoopCout = (*bp).LoopCout; //最大循环次数
int i, j, k, n,d;
double temp;
double e = f + 1;
double c;
double Ep[10000];

for (i = 0; i < IN_COUT; i++) // 复制结构体中的权矩阵
for (j = 0; j < h; j++)
v[i][j] = (*bp).v[i][j];
for (i = 0; i < h; i++)
for (j = 0; j < OUT_COUT; j++)
w[i][j] = (*bp).w[i][j];
for (n = 0; e > f && n < LoopCout; n++)
{ //对每个样本训练网络
e = 0;
for (i= 0; i < COUT; i++)
{
for (k= 0; k < h; k++)
{ //计算隐层输出向量
temp = 0;
for (j = 0; j < IN_COUT; j++)
temp = temp + x[i][j] * v[j][k];
O1[k] = fnet(temp);
}
for (k = 0; k < OUT_COUT; k++)
{ //计算输出层输出向量
temp = 0;
for (j = 0; j < h; j++)
temp = temp + O1[j] * w[j][k];
O2[k] = fnet(temp);
}
for (j = 0; j < OUT_COUT; j++) //计算输出层的权修改量
ChgO[j] = O2[j] * (1 - O2[j]) * (y[i][j] - O2[j]);
for (j = 0; j < OUT_COUT ; j++) //计算输出误差
e = e + (y[i][j] - O2[j]) * (y[i][j] - O2[j]);
Ep[n]=e;
for (j = 0; j < h; j++)
{ //计算隐层权修改量
temp = 0;
for (k = 0; k < OUT_COUT; k++)
temp = temp + w[j][k] * ChgO[k];
ChgH[j] = temp * O1[j] * (1 - O1[j]);
}
for (j = 0; j < h; j++) //修改输出层权矩阵
for (k = 0; k < OUT_COUT; k++)
w[j][k] = w[j][k] + a * O1[j] * ChgO[k];
for (j = 0; j < IN_COUT; j++)
for (k = 0; k < h; k++)
v[j][k] = v[j][k] + a * x[i][j] * ChgH[k];
}
if (n % 10 == 0)
printf("误差 : %f\n", Ep[n]);

}
printf("总共循环次数：%d\n", n);
printf("调整后的隐层权矩阵：\n");
for (i = 0; i < IN_COUT; i++) {
for (j = 0; j < h; j++)
printf("%f ", v[i][j]);
printf("\n");
}
printf("调整后的输出层权矩阵：\n");
for (i = 0; i < h; i++) {
for (j = 0; j < OUT_COUT; j++)
printf("%f ", w[i][j]);
printf("\n");
}
for (i = 0; i < IN_COUT; i++) //把结果复制回结构体
for (j = 0; j < h; j++)
(*bp).v[i][j] = v[i][j];
for (i = 0; i < h; i++)
for (j = 0; j < OUT_COUT; j++)
(*bp).w[i][j] = w[i][j];

initgraph(640, 480); //画误差曲线
for(d=100;d)
getchar(); // 按任意键继续
closegraph();

printf("bp网络训练结束！按回车关闭\n");
return 1;

}

int main()
{

float x[COUT][IN_COUT] = {{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}}; //训练样本
int y[COUT][OUT_COUT] = {{0,1},
{0,1},
{0,1},
{1,0},
{1,0},
{1,0}}; //理想输出
bp_nn bp;

InitBp(&bp); //初始化bp网络结构
TrainBp(&bp, x, y); //训练bp神经网络

return(0);

}