改进的BP神经网络算法(C语言源码)
#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 for(j=0;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 for(j=0;j fscanf(fp2,"%lf",&OutpointData[i][j]); fclose(fp2); } /*****************************************************/ /*****************************************归一化******************************************************/ void unitary() { int i,j; int k=0; for(j=0;j { InpointData_MAX[j]=InpointData[0][j]; InpointData_MIN[j]=InpointData[0][j]; for(i=0;i if(InpointData_MAX[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 { OutpointData_MAX[j]=OutpointData[0][j]; OutpointData_MIN[j]=OutpointData[0][j]; for(i=0;i if(OutpointData_MAX[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 for(i=0;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 for(i=0;i if(OutpointData_MAX[j]==0) OutpointData[i][j]=0; else OutpointData[i][j]=(OutpointData[i][j]-OutpointData_MIN[j]+A)/(OutpointData_MAX[j]-Outpoi ntData_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 for(i=0;i for(j=0;j { w[i][j]=(rand()*2.0/RAND_MAX-1)/2; dlta_w[i][j]=0; } for(i=0;i limen[i]=(rand()*2.0/RAND_MAX-1)/2; dlta_limen[i]=0; } for(i=0;i for(j=0;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 { double sum=0; for(j=0;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 { Outpoint_y[j]=0; for(i=0;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 s=0; for(j=0;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 { dlta_w[i][k]=ALFA*dlta_w[i][k]+BETA*defaultOutpoint[i]*(1-defaultOutpoint[i])*s*InpointDat a[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 for(j=0;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 for(j=0;j fscanf(fp3,"%lf",&tp[i][j]); fclose(fp3); for(j=0;j for(i=0;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 for(i=0;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 { 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 sss=0; //中间参数 for(t=0;t { e1[t]=0; for(i=0;i { double sum=0; for(j=0;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 { y[t][j]=0; for(i=0;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 { for(k=0;k fprintf(fp2," %lf ",w[i][k]); fprintf(fp2,"\n"); } fprintf(fp2,"\n"); for(i=0;i fprintf(fp2," %lf ",limen[i]); fprintf(fp2,"\n\n"); for(i=0;i { for(j=0;j fprintf(fp2," %lf ",v[j][i]); fprintf(fp2,"\n"); } fclose(fp2); }