bp神经网络c语言实现

素颜马尾好姑娘i 2022-05-18 10:57 293阅读 0赞

\#include "stdio.h"  
\#include "stdlib.h"  
\#include "time.h"  
\#include "math.h"  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*  
inpoints 为输入神经元个数，可改变  
outpoints为输出神经元个数  
defaultpoints为隐层神经元个数  
datagrough为样本数据个数  
\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*  
  
  
\*\*\*\*\*\*以下数据定义可以修改\*\*\*\*\*/  
\#define A 0  
\#define a 1  
\#define b 1  
\#define c 1  
\#define ALFA 0.85  
\#define BETA 0.2 //学习率0~1  
\#define Total 20000  
\#define inpoints 9  
\#define outpoints 5  
\#define defaultpoints 28  
\#define datagrough 44  
\#define forecastdata 4  
  
  
/\*\*\*\*\*\*\*\*\*\*定义所需变量\*\*\*\*\*\*\*\*/  
double InpointData\[datagrough\]\[inpoints\],OutpointData\[datagrough\]\[outpoints\]; /\* 输入输出数据 \*/  
double InpointData\_MAX\[inpoints\],InpointData\_MIN\[inpoints\]; /\* 每个因素最大数据 \*/  
double OutpointData\_MAX\[outpoints\],OutpointData\_MIN\[outpoints\]; /\* 每个因素最小数据 \*/  
double w\[defaultpoints\]\[inpoints\],limen\[defaultpoints\],v\[outpoints\]\[defaultpoints\]; /\* 连接权值、阈值 \*/  
double dlta\_w\[defaultpoints\]\[inpoints\],dlta\_limen\[defaultpoints\],dlta\_v\[outpoints\]\[defaultpoints\]; /\* 连接权、阈值修正值 \*/  
double defaultOutpoint\[defaultpoints\],Outpoint\_dp\[outpoints\],Outpoint\_ep\[datagrough\];  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*读数据文件\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*/  
void ReadData()  
\{  
FILE \*fp1,\*fp2;  
int i,j;  
if((fp1=fopen("D:\\\\data\\\\训练输入.txt","r"))==NULL)  
\{  
printf("1can not open the file\\n");  
exit(0);  
\}  
for(i=0;i<datagrough;i++)  
for(j=0;j<inpoints;j++)  
fscanf(fp1,"%lf",&InpointData\[i\]\[j\]);  
fclose(fp1);  
  
  
if((fp2=fopen("D:\\\\data\\\\训练输出.txt","r"))==NULL)  
\{  
printf("2can not open the file\\n");  
exit(0);  
\}  
for(i=0;i<datagrough;i++)  
for(j=0;j<outpoints;j++)  
fscanf(fp2,"%lf",&OutpointData\[i\]\[j\]);  
fclose(fp2);  
\}  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*/  
  
  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*归一化\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*/  
void unitary()  
\{  
int i,j;  
int k=0;  
for(j=0;j<inpoints;j++) //找出每列的最大、最小值存放在数组InpointData\_MAX\[j\]、InpointData\_MIN\[j\]中  
\{  
InpointData\_MAX\[j\]=InpointData\[0\]\[j\];  
InpointData\_MIN\[j\]=InpointData\[0\]\[j\];  
for(i=0;i<datagrough;i++)  
if(InpointData\_MAX\[j\]<InpointData\[i\]\[j\])  
InpointData\_MAX\[j\]=InpointData\[i\]\[j\];  
else if(InpointData\_MIN\[j\]>InpointData\[i\]\[j\])  
InpointData\_MIN\[j\]=InpointData\[i\]\[j\];  
\}  
  
  
for(j=0;j<outpoints;j++) //找出每列的最大、最小值存放在数组OutpointData\_MAX\[j\]、OutpointData\_MIN\[j\]中  
\{  
OutpointData\_MAX\[j\]=OutpointData\[0\]\[j\];  
OutpointData\_MIN\[j\]=OutpointData\[0\]\[j\];  
for(i=0;i<datagrough;i++)  
if(OutpointData\_MAX\[j\]<OutpointData\[i\]\[j\])  
OutpointData\_MAX\[j\]=OutpointData\[i\]\[j\];  
else if(OutpointData\_MIN\[j\]>OutpointData\[i\]\[j\])  
OutpointData\_MIN\[j\]=OutpointData\[i\]\[j\];  
\}  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*将数据归一处理,处理之后的数据全部在\[0，1\]之间\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*/  
for(j=0;j<inpoints;j++)  
for(i=0;i<datagrough;i++)  
if(InpointData\_MAX\[j\]==0)  
InpointData\[i\]\[j\]=0;  
else  
InpointData\[i\]\[j\]=(InpointData\[i\]\[j\]-InpointData\_MIN\[j\]+A)/(InpointData\_MAX\[j\]-InpointData\_MIN\[j\]+A);  
  
  
for(j=0;j<outpoints;j++)  
for(i=0;i<datagrough;i++)  
if(OutpointData\_MAX\[j\]==0)  
OutpointData\[i\]\[j\]=0;  
else  
OutpointData\[i\]\[j\]=(OutpointData\[i\]\[j\]-OutpointData\_MIN\[j\]+A)/(OutpointData\_MAX\[j\]-OutpointData\_MIN\[j\]+A);  
  
  
\}  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*/  
  
  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*初始化，随机赋初值\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*/  
void Initialization()  
\{  
int i,j;  
srand((unsigned)time(NULL)); //头文件名 \#include <time.h>  
  
  
for(i=0;i<defaultpoints;i++) //给输入层到隐层的连接权赋随机值LianJie\_w\[i\]\[j\],这些值在\[0,1\]  
for(j=0;j<inpoints;j++)  
\{  
w\[i\]\[j\]=(rand()\*2.0/RAND\_MAX-1)/2;  
dlta\_w\[i\]\[j\]=0;  
\}  
  
  
for(i=0;i<defaultpoints;i++)  
\{  
limen\[i\]=(rand()\*2.0/RAND\_MAX-1)/2;  
dlta\_limen\[i\]=0;  
\}  
  
  
for(i=0;i<outpoints;i++) //给隐层到输出层的连接权赋初值  
for(j=0;j<defaultpoints;j++)  
\{  
v\[i\]\[j\]=(rand()\*2.0/RAND\_MAX-1)/2;  
dlta\_v\[i\]\[j\]=0;  
\}  
\}  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*求单样本的计算输出误差\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*/  
void out\_sub1(int t)  
\{  
int i,j;  
double defaultInpoint\[defaultpoints\];  
double Outpoint\_y\[outpoints\];  
Outpoint\_ep\[t\]=0;  
for(i=0;i<defaultpoints;i++)  
\{  
double sum=0;  
for(j=0;j<inpoints;j++)  
sum+=w\[i\]\[j\]\*InpointData\[t\]\[j\];  
defaultInpoint\[i\]=sum+limen\[i\];  
defaultOutpoint\[i\]=1/(a+b\*exp(-1\*c\*defaultInpoint\[i\]));//求O\[i\]  
\}  
  
  
for(j=0;j<outpoints;j++)//求Y\[i\]  
\{  
Outpoint\_y\[j\]=0;  
for(i=0;i<defaultpoints;i++)  
Outpoint\_y\[j\]+=v\[j\]\[i\]\*defaultOutpoint\[i\];  
Outpoint\_dp\[j\]=OutpointData\[t\]\[j\]-Outpoint\_y\[j\];  
Outpoint\_ep\[t\]+=Outpoint\_dp\[j\]\*Outpoint\_dp\[j\]/2;  
\}  
\}  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*反算权值\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*/  
void out\_sub2(int t)  
\{  
int i,j,k;  
double s;  
for(i=0;i<defaultpoints;i++)  
\{  
s=0;  
for(j=0;j<outpoints;j++)  
\{  
dlta\_v\[j\]\[i\]=ALFA\*dlta\_v\[j\]\[i\]+BETA\*Outpoint\_dp\[j\]\*defaultOutpoint\[i\]; //  
s+=v\[j\]\[i\]\*Outpoint\_dp\[j\];  
v\[j\]\[i\]+=dlta\_v\[j\]\[i\];  
\}  
dlta\_limen\[i\]=ALFA\*dlta\_limen\[i\]+BETA\*defaultOutpoint\[i\]\*(1-defaultOutpoint\[i\])\*s;//  
limen\[i\]+=dlta\_limen\[i\];  
for(k=0;k<inpoints;k++)  
\{  
dlta\_w\[i\]\[k\]=ALFA\*dlta\_w\[i\]\[k\]+BETA\*defaultOutpoint\[i\]\*(1-defaultOutpoint\[i\])\*s\*InpointData\[t\]\[k\];//  
w\[i\]\[k\]=w\[i\]\[k\]+dlta\_w\[i\]\[k\];  
\}  
\}  
\}  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*/  
void forecast()  
\{  
int i,j,t,k=0;  
double e,e1\[forecastdata\]=\{0\}; //训练误差  
double sss;  
double InputData\_x\[forecastdata\]\[inpoints\],tp\[forecastdata\]\[outpoints\];  
double defInpoint,defOutpoint\[defaultpoints\],y\[forecastdata\]\[outpoints\];//y\[forecastdata\]\[outpoints\]为网络检验输出  
FILE \*fp1,\*fp3;  
if((fp1=fopen("D:\\\\data\\\\预测输入.txt","r"))==NULL) //检验数据输入  
\{  
printf("3can not open the file\\n");  
exit(0);  
\}  
for(i=0;i<forecastdata;i++)  
for(j=0;j<inpoints;j++)  
fscanf(fp1,"%lf",&InputData\_x\[i\]\[j\]);  
fclose(fp1);  
  
  
if((fp3=fopen("D:\\\\data\\\\预测输出.txt","r"))==NULL) //实际检验结果输出  
\{  
printf("31can not open the file\\n");  
exit(0);  
\}  
for(i=0;i<forecastdata;i++)  
for(j=0;j<outpoints;j++)  
fscanf(fp3,"%lf",&tp\[i\]\[j\]);  
fclose(fp3);  
  
  
for(j=0;j<inpoints;j++) // 检验数据归一化  
for(i=0;i<forecastdata;i++)  
if(InpointData\_MAX\[j\]==0)  
InputData\_x\[i\]\[j\]=0;  
else  
InputData\_x\[i\]\[j\]=(InputData\_x\[i\]\[j\]-InpointData\_MIN\[j\]+A)/(InpointData\_MAX\[j\]-InpointData\_MIN\[j\]+A);  
  
  
for(j=0;j<outpoints;j++)  
for(i=0;i<forecastdata;i++)  
if(OutpointData\_MAX\[j\]==0)  
tp\[i\]\[j\]=0;  
else  
tp\[i\]\[j\]=(tp\[i\]\[j\]-OutpointData\_MIN\[j\]+A)/(OutpointData\_MAX\[j\]-OutpointData\_MIN\[j\]+A);  
  
  
do  
\{  
Initialization(); //初始化连接权值w\[i\]\[j\],limen\[i\],v\[k\]\[i\]  
k=0;  
do  
\{  
e=0;  
for(t=0;t<datagrough;t++)  
\{  
out\_sub1(t); //正向计算网络输出  
out\_sub2(t); //反向计算，修正权值  
e+=Outpoint\_ep\[t\]; //计算输出误差  
\}  
k++;  
\}while((k<Total)&&(e>0.1));  
sss=0; //中间参数  
for(t=0;t<forecastdata;t++)  
\{  
e1\[t\]=0;  
for(i=0;i<defaultpoints;i++)  
\{  
double sum=0;  
for(j=0;j<inpoints;j++)  
sum+=w\[i\]\[j\]\*InputData\_x\[t\]\[j\];  
defInpoint=sum+limen\[i\];  
defOutpoint\[i\]=1/(a+b\*exp(-1\*c\*defInpoint));  
\}  
for(j=0;j<outpoints;j++)  
\{  
y\[t\]\[j\]=0;  
for(i=0;i<defaultpoints;i++)  
y\[t\]\[j\]+=v\[j\]\[i\]\*defOutpoint\[i\];  
e1\[t\]+=(y\[t\]\[j\]-tp\[t\]\[j\])\*(y\[t\]\[j\]-tp\[t\]\[j\])/2;  
y\[t\]\[j\]=y\[t\]\[j\]\*(OutpointData\_MAX\[j\]-OutpointData\_MIN\[j\]+A)+OutpointData\_MIN\[j\]-A;  
\}  
sss+=e1\[t\];  
\}  
sss=sss/forecastdata;  
printf(" %lf %lf\\n",e,sss);  
\}while(sss>0.12);  
\}  
/\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*/  
  
  
  
  
void main()  
\{  
int i,j,k;  
FILE \*fp2;  
ReadData(); //读训练数据：输入和输出  
unitary(); //归一化，将输入输出数据归一，结果在\[0，1\]中  
forecast(); //检验误差  
if((fp2=fopen("D:\\\\data\\\\计算权值.txt","w"))==NULL) //文件输出训练好的权值  
\{  
printf("6can not open the file\\n");  
exit(0);  
\}  
for(i=0;i<defaultpoints;i++)  
\{  
for(k=0;k<inpoints;k++)  
fprintf(fp2," %lf ",w\[i\]\[k\]);  
fprintf(fp2,"\\n");  
\}  
fprintf(fp2,"\\n");  
for(i=0;i<defaultpoints;i++)  
fprintf(fp2," %lf ",limen\[i\]);  
fprintf(fp2,"\\n\\n");  
for(i=0;i<defaultpoints;i++)  
\{  
for(j=0;j<outpoints;j++)  
fprintf(fp2," %lf ",v\[j\]\[i\]);  
fprintf(fp2,"\\n");  
\}  
fclose(fp2);  
  
\}