调试模糊神经网络的中间产物:
/// 五层结构/ #define InputN (2)
#define HideN_0 (InputN*2)
#define HideN_1 (InputN*2)#define HideN_2 (InputN*2)#define OutN (1)
typedef struct __FuzzyBP_DATA__{ double Input[InputN]; double Teach[OutN];}FuzzyBP_DATA_t;
typedef struct __FuzzyBP_t__
{ double y[OutN];double xOut[InputN];
double hOut_0[HideN_0]; double hOut_1[HideN_1]; double hOut_2[HideN_2]; double yOut[OutN];double h0_delta[HideN_0];
double h1_delta[HideN_1]; double h2_delta[HideN_2]; double y_delta[OutN]; double wh0[InputN][HideN_0]; // 权值 double wh1[HideN_0][HideN_1]; // 权值 double wh2[HideN_1][HideN_2]; // 权值 double v[HideN_2][OutN]; //double deltawh0[InputN][HideN_0]; // 权值
double deltawh1[HideN_0][HideN_1]; // 权值 double deltawh2[HideN_1][HideN_2]; // 权值 double deltav[HideN_2][OutN]; // double err; double errLimit;double alpha;
double beta;int maxLoopNum;
}FuzzyBP_t;double fuction_lizhu(double x, double c,double u){ double tmp ; tmp = ( (x - c)*(x - c) ) /(u*u); tmp = exp(-tmp); return tmp;}
double fuction_lizhuC(double x, double c,double u)
{ double tmp ; tmp = ( (x - c)*(x - c) ) /(u*u); tmp = exp( -tmp)*2*(x - c)/(u*u); return tmp;}double fuction_lizhuU(double x, double c,double u){ double tmp ; tmp = ( (x - c)*(x - c) ) /(u*u); tmp = -exp( -tmp)*2*(x - c)*(x - c)/(u*u*u); return tmp;} double fuction_sigmod(double val){ return 1/(1 + exp(-val));}
void FuzzyBP_XunLian(FuzzyBP_t *pBp, FuzzyBP_DATA_t *pData, int dataNum)
{for (int i=0; i<InputN; i++)
{ for (int j = 0; j<HideN_0; j++) { pBp->wh0[i][j] = (rand()/32767.0)*2 - 1; pBp->deltawh0[i][j] = 0.0; } } for (int i=0; i<HideN_0; i++) { for (int j = 0; j<HideN_1; j++) { pBp->wh1[i][j] = (rand()/32767.0)*2 - 1; pBp->deltawh1[i][j] = 0.0; } }for (int i=0; i<HideN_1; i++)
{ for (int j = 0; j<HideN_2; j++) { pBp->wh2[i][j] = (rand()/32767.0)*2 - 1; pBp->deltawh2[i][j] = 0.0; } } for (int i=0; i<HideN_0; i++) { for (int j = 0; j<OutN; j++) { pBp->v[i][j] = (rand()/32767.0)*2 - 1; pBp->deltav[i][j] = 0.0; } } int loop = 0; while( loop < pBp->maxLoopNum) {pBp->err = 0.0;
for (int m = 0; m<dataNum; m++)
{for (int i=0; i<OutN; i++)
{ pBp->y[i] = pData[m].Teach[i]; } / // 正向传播 / // 输入层 for (int i=0; i<InputN; i++) { pBp->xOut[i] = pData[m].Input[i]; }// 隐藏层
for (int i=0; i<HideN_0; i++) { double sumTemp = 0.0; for (int j=0; j<InputN; j++) { sumTemp += pBp->wh0[j][i] * pBp->xOut[j]; } pBp->hOut_0[i] = tanh(sumTemp); } for (int i=0; i<HideN_1; i++) { double sumTemp = 0.0; for (int j=0; j<HideN_0; j++) { sumTemp += pBp->wh1[j][i] * pBp->hOut_0[j]; } pBp->hOut_1[i] = tanh(sumTemp); } for (int i=0; i<HideN_2; i++) { double sumTemp = 0.0; for (int j=0; j<HideN_1; j++) { sumTemp += pBp->wh2[j][i] * pBp->hOut_1[j]; } pBp->hOut_2[i] = tanh(sumTemp); } // 输出层 for (int i=0; i<OutN; i++) { double sumTemp = 0.0; for (int j=0; j<HideN_2; j++) { sumTemp += pBp->v[j][i] * pBp->hOut_2[j]; } pBp->yOut[i] = fuction_sigmod(sumTemp); }/ // 误差传播 / for (int i=0; i< OutN; i++) { double errTemp = pBp->y[i] - pBp->yOut[i]; pBp->y_delta[i] = errTemp * fuction_sigmod(pBp->yOut[i]) * ( 1.0 - fuction_sigmod(pBp->yOut[i])); pBp->err += errTemp * errTemp; } for (int i=0; i<HideN_2; i++) { double errTemp = 0.0; for (int j=0; j<OutN; j++) { errTemp += pBp->y_delta[j] * pBp->v[i][j]; } pBp->h2_delta[i] = errTemp * (1-tanh(pBp->hOut_2[i])*tanh(pBp->hOut_2[i])) ; } for (int i=0; i<HideN_1; i++) { double errTemp = 0.0; for (int j=0; j<HideN_2; j++) { errTemp += pBp->h2_delta[j] * pBp->wh2[i][j]; } pBp->h1_delta[i] = errTemp * (1-tanh(pBp->hOut_1[i])*tanh(pBp->hOut_1[i])) ; } for (int i=0; i<HideN_0; i++) { double errTemp = 0.0; for (int j=0; j<HideN_1; j++) { errTemp += pBp->h1_delta[j] * pBp->wh1[i][j]; } pBp->h0_delta[i] = errTemp * (1-tanh(pBp->hOut_0[i])*tanh(pBp->hOut_0[i])) ; } / // 参数修正 / for (int i=0; i<OutN; i++) { for (int j = 0; j<HideN_2; j++) { pBp->deltav[j][i] = pBp->alpha * pBp->deltav[j][i] + pBp->beta * pBp->y_delta[i] * pBp->hOut_2[j]; pBp->v[j][i] += pBp->deltav[j][i]; } }
for (int i=0; i<HideN_2; i++)
{ for (int j = 0; j<HideN_1; j++) { pBp->deltawh2[j][i] = pBp->alpha * pBp->deltawh2[j][i] + pBp->beta * pBp->h2_delta[i] * pBp->hOut_1[j]; pBp->wh2[j][i] += pBp->deltawh2[j][i]; } } for (int i=0; i<HideN_1; i++) { for (int j = 0; j<HideN_0; j++) { pBp->deltawh1[j][i] = pBp->alpha * pBp->deltawh1[j][i] + pBp->beta * pBp->h1_delta[i] * pBp->hOut_0[j]; pBp->wh1[j][i] += pBp->deltawh1[j][i]; } }for (int i=0; i<HideN_0; i++)
{ for (int j = 0; j<InputN; j++) { pBp->deltawh0[j][i] = pBp->alpha * pBp->deltawh0[j][i] + pBp->beta * pBp->h0_delta[i] * pBp->xOut[j]; pBp->wh0[j][i] += pBp->deltawh0[j][i]; } }
}
pBp->err = pBp->err/2;
if (pBp->err < pBp->errLimit) { CString tmp; tmp.Format("学习成功, 已经收敛! 训练次数： %6d", loop); AfxMessageBox(tmp); break; } loop++; }} void FuzzyBP_JianYan(FuzzyBP_t *pBp, FuzzyBP_DATA_t *pData){ for (int i=0; i<InputN; i++) { pBp->xOut[i] = pData->Input[i]; }
for (int i=0; i<HideN_0; i++)
{ double sumTemp = 0.0; for (int j=0; j<InputN; j++) { sumTemp += pBp->wh0[j][i] * pBp->xOut[j]; } pBp->hOut_0[i] = tanh(sumTemp); }for (int i=0; i<HideN_1; i++)
{ double sumTemp = 0.0; for (int j=0; j<HideN_0; j++) { sumTemp += pBp->wh1[j][i] * pBp->hOut_0[j]; } pBp->hOut_1[i] = tanh(sumTemp); } for (int i=0; i<HideN_2; i++) { double sumTemp = 0.0; for (int j=0; j<HideN_1; j++) { sumTemp += pBp->wh2[j][i] * pBp->hOut_1[j]; } pBp->hOut_2[i] = tanh(sumTemp); }// 输出层
for (int i=0; i<OutN; i++) { double sumTemp = 0.0; for (int j=0; j<HideN_2; j++) { sumTemp += pBp->v[j][i] * pBp->hOut_2[j]; } pBp->yOut[i] = fuction_sigmod(sumTemp); }
}
int _tmain(int argc, TCHAR* argv[], TCHAR* envp[])
{ int nRetCode = 0;FuzzyBP_DATA_t data[15] = { { {1.780, 1.140 },1}, { {1.960, 1.180 },1}, { {1.860, 1.200 },1}, { {1.720, 1.240 },0}, { {2.000, 1.260 },1}, { {2.000, 1.280 },1}, { {1.960, 1.300 },1}, { {1.740, 1.360 },0},
{ {1.640, 1.380 },0},
{ {1.820, 1.380 },0}, { {1.900, 1.380 },0}, { {1.700, 1.400 },0}, { {1.820, 1.480 },0}, { {1.820, 1.540 },0}, { {2.080, 1.560 },0}, }; FuzzyBP_t *pBp = new FuzzyBP_t; pBp->alpha = 0.10; pBp->beta = 0.86; pBp->err = 100.0; pBp->errLimit = 0.0001; pBp->maxLoopNum = 1000;// pBp->alpha = 0.90;
// pBp->beta = 0.26;// pBp->errLimit = 0.0001;// pBp->maxLoopNum = 5000;// FuzzyBP_XunLian(pBp, data, 15);printf(" 误差 %6.4f \n", pBp->err);
for (int i=0; i<15; i++) { printf("%3d %8.5f %8.5f %8.5f ", i, data[i].Input[0], data[i].Input[1], data[i].Teach[0]); FuzzyBP_JianYan(pBp,&data[i]);printf(" %8.5f \n", data[i].Teach[0]);
}
system("pause");
return nRetCode;}