#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;elseInpointData[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;elseOutpointData[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 <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*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];doubledefInpoint,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;elseInputData_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;elsetp[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);}。