机器学习 之 模糊神经（失败总结）

弄了一段时间，效果很差！ 感觉对其理解不够！看了一些论文，  感觉有点前后矛盾，写了一段时间代码，感觉无法完成！

这里做一个总结，说明为什么完成不了！

1. 隶属度  我是用 高其隶属度函数， 感觉公式，导数计算，偏导计算，都已经完成，但误差传播，与，修正时候，总是出错！

2. 对我的项目感觉没有增益！ 我是想用在麻醉深度的CSI训练上，但，深度， 需要用，4个特征量，六个深度，如果按模糊的方式，会有 6*6*6*6的专家规则，超过预期！

3. 看了很多论文模糊神经的最经常用的是控制，及 电机控制，无功补偿，洗衣机时间控制之类，有明确的专家规则，有明确的输出，这样训练时候，可以很好的控制。

4. 麻醉深度，无法固定规则可以遵寻，训练也就无重谈起。

下面是代码：注（运行不成功）


#include <math.h>

/////////////////////////////
// 五层结构
/////////////////////////////


#define InputN (2)

#define HideN_0 (2)
#define HideN_1 (4)
#define HideN_2 (4)

#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 FuzzyC[HideN_0]; //---
double FuzzyU[HideN_0]; //---

double deltaFuzzyC[HideN_0];
double deltaFuzzyU[HideN_0];


double RuleC[HideN_2];
double RuleU[HideN_2];

double deltaRuleC[HideN_2];
double deltaRuleU[HideN_2];

double err;
double errLimit;

double alpha;
double beta;

int maxLoopNum;
}FuzzyBP_t;


double fuction_lishu(double x, double c,double u) // 高斯隶属函数
{
double tmp ;
tmp = exp( -(x - c)*(x - c) /(u*u) );
return tmp;
}
double fuction_dao_lishu(double x, double c,double u) // 高斯隶属函数导数
{
double tmp ;
tmp = exp( -(x - c)*(x - c) /(u*u) );
tmp *= -2*(x - c) /(u*u);
return tmp;
}
double fuction_lishuC(double x, double c,double u) // 高斯隶属函数偏导数 -- c
{
double tmp ;
tmp = exp( -(x - c)*(x - c) /(u*u) );
tmp *= 2*(x - c)/(u*u);
return tmp;
}

double fuction_lishuU(double x, double c,double u) // 高斯隶属函数偏导数 -- u
{
double tmp ;
tmp = exp( -(x - c)*(x - c) /(u*u) );
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<HideN_0; i++)
{
pBp->FuzzyC[i] = (rand()/32767.0)*2 - 1;
pBp->FuzzyU[i] = (rand()/32767.0)*2 - 1;
pBp->deltaFuzzyC[i] = 0.0;
pBp->deltaFuzzyU[i] = 0.0;

}


for (int i=0; i<HideN_2; i++)
{
pBp->RuleC[i] = (rand()/32767.0)*2 - 1;
pBp->RuleU[i] = (rand()/32767.0)*2 - 1;
pBp->deltaRuleC[i] = 0.0;
pBp->deltaRuleU[i] = 0.0;

}

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_2; 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_0; i++)
{
pBp->hOut_0[i] = fuction_lishu(pBp->xOut[i], pBp->FuzzyC[i],pBp->FuzzyU[i]);
}


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;
}



/////////////////////////////////////
// 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];
// }
// }


for (int i=0; i<HideN_0; i++)
{
pBp->deltaFuzzyC[i] = pBp->alpha * pBp->deltaFuzzyC[i] + pBp->beta * pBp->h0_delta[i] * fuction_lishuC(pBp->xOut[i], pBp->FuzzyC[i], pBp->FuzzyU[i]);
pBp->FuzzyC[i] += pBp->deltaFuzzyC[i];

pBp->deltaFuzzyU[i] = pBp->alpha * pBp->deltaFuzzyU[i] + pBp->beta * pBp->h0_delta[i] * fuction_lishuU(pBp->xOut[i], pBp->FuzzyC[i], pBp->FuzzyU[i]);
pBp->FuzzyU[i] += pBp->deltaFuzzyU[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];
}

// 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_0; i++)
{
pBp->hOut_0[i] = fuction_lishu(pBp->xOut[i], pBp->FuzzyC[i],pBp->FuzzyU[i]);
}

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);

pData->Teach[i] = pBp->yOut[i];
}

}

int _tmain(int argc, TCHAR* argv[], TCHAR* envp[])
{
int nRetCode = 0;

srand(time(NULL));


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 = 1.00;
pBp->beta = 0.86;
pBp->err = 100.0;
pBp->errLimit = 0.0001;
pBp->maxLoopNum = 30000;

// 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;
}

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原文地址：https://www.cnblogs.com/signal/p/2888781.html