c++调用matlab神经网络,C++实现的BP神经网络代码

#pragma hdrstop

#include

#include

const A=30.0;

const B=10.0;

const MAX=500;　　　　　　　　　　　　 //最大训练次数

const COEF=0.0035; //网络的学习效率

const BCOEF=0.001;//网络的阀值调整效率

const ERROR=0.002　　 ; // 网络训练中的允许误差

const ACCURACY=0.0005;//网络要求精度

double sample[41][4]={{0,0,0,0},{5,1,4,19.020},{5,3,3,14.150},

{5,5,2,14.360},{5,3,3,14.150},{5,3,2,15.390},

{5,3,2,15.390},{5,5,1,19.680},{5,1,2,21.060},

{5,3,3,14.150},{5,5,4,12.680},{5,5,2,14.360},

{5,1,3,19.610},{5,3,4,13.650},{5,5,5,12.430},

{5,1,4,19.020},{5,1,4,19.020},{5,3,5,13.390},

{5,5,4,12.680},{5,1,3,19.610},{5,3,2,15.390},

{1,3,1,11.110},{1,5,2,6.521},{1,1,3,10.190},

{1,3,4,6.043},{1,5,5,5.242},{1,5,3,5.724},

{1,1,4,9.766},{1,3,5,5.870},{1,5,4,5.406},

{1,1,3,10.190},{1,1,5,9.545},{1,3,4,6.043},

{1,5,3,5.724},{1,1,2,11.250},{1,3,1,11.110},

{1,3,3,6.380},{1,5,2,6.521},{1,1,1,16.000},

{1,3,2,7.219},{1,5,3,5.724}};

double w[4][10][10],wc[4][10][10],b[4][10],bc[4][10];

double o[4][10],netin[4][10],d[4][10],differ;//单个样本的误差

double is; //全体样本均方差

int count,a;

void netout(int m, int n);//计算网络隐含层和输出层的输出

void calculd(int m,int n); //计算网络的反向传播误差

void calcalwc(int m,int n);//计算网络权值的调整量

void calcaulbc(int m,int n); //计算网络阀值的调整量

void changew(int m,int n); //调整网络权值

void changeb(int m,int n);//调整网络阀值

void clearwc(int m,int n);//清除网络权值变化量wc

void clearbc(int m,int n);//清除网络阀值变化量bc

void initialw(void);//初始化NN网络权值W

void initialb(void); //初始化NN网络阀值

void calculdiffer(void);//计算NN网络单个样本误差

void calculis(void);//计算NN网络全体样本误差

void trainNN(void);//训练NN网络

/*计算NN网络隐含层和输出层的输出 */

void netout(int m,int n)

{

int i,j,k;

//隐含层各节点的的输出

for (j=1,i=2;j<=m;j++) //m为隐含层节点个数

{

netin[i][j]=0.0;

for(k=1;k<=3;k++)//隐含层的每个节点均有三个输入变量

netin[i][j]=netin[i][j]+o[i-1][k]*w[i][k][j];

netin[i][j]=netin[i][j]-b[i][j];

o[i][j]=A/(1+exp(-netin[i][j]/B));

}

//输出层各节点的输出

for (j=1,i=3;j<=n;j++)

{

netin[i][j]=0.0;

for (k=1;k<=m;k++)

netin[i][j]=netin[i][j]+o[i-1][k]*w[i][k][j];

netin[i][j]=netin[i][j]-b[i][j];

o[i][j]=A/(1+exp(-netin[i][j]/B)) ;

}

}

/*计算NN网络的反向传播误差*/

void calculd(int m,int n)

{

int i,j,k;

double t;

a=count-1;

d[3][1]=(o[3][1]-sample[a][3])*(A/B)*exp(-netin[3][1]/B)/pow(1+exp(-netin[3][1]/B),2);

//隐含层的误差

for (j=1,i=2;j<=m;j++)

{

t=0.00;

for (k=1;k<=n;k++)

t=t+w[i+1][j][k]*d[i+1][k];

d[i][j]=t*(A/B)*exp(-netin[i][j]/B)/pow(1+exp(-netin[i][j]/B),2);

}

}

/*计算网络权值W的调整量*/

void calculwc(int m,int n)

{

int i,j,k;

// 输出层(第三层)与隐含层(第二层)之间的连接权值的调整

for (i=1,k=3;i<=m;i++)

{

for (j=1;j<=n;j++)

{

wc[k][i][j]=-COEF*d[k][j]*o[k-1][i]+0.5*wc[k][i][j];

}

// printf("\n");

}

//隐含层与输入层之间的连接权值的调整

for (i=1,k=2;i<=m;i++)

{

for (j=1;j<=m;j++)

{

wc[k][i][j]=-COEF*d[k][j]*o[k-1][i]+0.5*wc[k][i][j];

}

//　　 printf("\n");

}

}

/*计算网络阀值的调整量*/

void calculbc(int m,int n)

{

int j;

for (j=1;j<=m;j++)

{

bc[2][j]=BCOEF*d[2][j];

}

for (j=1;j<=n;j++)

{

bc[3][j]=BCOEF*d[3][j];

}

}

/*调整网络权值*/

void changw(int m,int n)

{

int i,j;

for (i=1;i<=3;i++)

for (j=1;j<=m;j++)

{

w[2][i][j]=0.9*w[2][i][j]+wc[2][i][j];

//为了保证系统有较好的鲁棒性，计算权值时乘惯性系数0.9

printf("w[2][%d][%d]=%f\n",i,j,w[2][i][j]);

}

for (i=1;i<=m;i++)

for (j=1;j<=n;j++)

{

w[3][i][j]=0.9*w[3][i][j]+wc[3][i][j];

printf("w[3][%d][%d]=%f\n",i,j,w[3][i][j]);

}

}

/*调整网络阀值*/

void changb(int m,int n)

{

int j;

for (j=1;j<=m;j++)

b[2][j]=b[2][j]+bc[2][j];

for (j=1;j<=n;j++)

b[3][j]=b[3][j]+bc[3][j];

}

/*清除网络权值变化量wc*/

void clearwc(void)

{

for (int i=0;i<4;i++)

for (int j=0;j<10;j++)

for (int k=0;k<10;k++)

wc[i][j][k]=0.00;

}

/*清除网络阀值变化量*/

void clearbc(void)

{

for (int i=0;i<4;i++)

for (int j=0;j<10;j++)

bc[i][j]=0.00;

}

/*初始化网络权值W*/

void initialw(void)

{

int i,j,k,x;

double weight;

for (i=0;i<4;i++)

for (j=0;j<10;j++)

for (k=0;k<10;k++)

{

randomize();

x=100+random(400);

weight=(double)x/5000.00;

w[i][j][k]=weight;

}

}

/*初始化网络阀值*/

void initialb(void)

{

int i,j,x;

double fazhi;

for (i=0;i<4;i++)

for (j=0;j<10;j++)

{

randomize();

for (int k=0;k<12;k++)

{

x=100+random(400);

}

fazhi=(double)x/50000.00;

b[i][j]=fazhi;

}

}

/*计算网络单个样本误差*/

void calculdiffer(void)

{

a=count-1;

differ=0.5*(o[3][1]-sample[a][3])*(o[3][1]-sample[a][3]);

}

void calculis(void)

{

int i;

is=0.0;

for (i=0;i<=19;i++)

{

o[1][1]=sample[i][0];

o[1][2]=sample[i][1];

o[1][3]=sample[i][2];

netout(8,1);

is=is+(o[3][1]-sample[i][3])*(o[3][1]-sample[i][3]);

}

is=is/20;

}

/*训练网络*/

void trainNN(void)

{

long int time;

int i,x[4];

initialw();

initialb();

for (time=1;time<=MAX;time++)

{

count=0;

while(count<=40)

{

o[1][1]=sample[count][0];

o[1][2]=sample[count][1];

o[1][3]=sample[count][2];

count=count+1;

clearwc();

clearbc();

netout(8,1);

calculdiffer();

while(differ>ERROR)

{

calculd(8,1);

calculwc(8,1);

calculbc(8,1);

changw(8,1);

changb(8,1);

netout(8,1);

calculdiffer();

}

}

printf("This is %d times training NN...\n",time);

calculis();

printf("is==%f\n",is);

if (is

}

}

//---------------------------------------------------------------------------

#pragma argsused

int main(int argc, char* argv[])

{

double result;

int m,test[4];

char ch='y';

cout<

trainNN();

cout<

while(ch=='y' || ch=='Y')

{

cout<

for (m=1;m<=3;m++)

cin>>test[m];

ch=getchar();

o[1][1]=test[1];

o[1][2]=test[2];

o[1][3]=test[3];

netout(8,1);

result=o[3][1];

printf("Final result is %f.\n",result);

printf("Still test?[Yes] or [No]\n");

ch=getchar();

}

return 0;

}