改进的BP神经网络算法(C语言源码).doc

#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01#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 InpointDatadatagroughinpoints,OutpointDatadatagroughoutpoints; /* 输入输出数据 */double InpointData_MAXinpoints,InpointData_MINinpoints; /* 每个因素最大数据 */double OutpointData_MAXoutpoints,OutpointData_MINoutpoints; /* 每个因素最小数据 */double wdefaultpointsinpoints,limendefaultpoints,voutpointsdefaultpoints; /* 连接权值、阈值 */double dlta_wdefaultpointsinpoints,dlta_limendefaultpoints,dlta_voutpointsdefaultpoints; /* 连接权、阈值修正值 */double defaultOutpointdefaultpoints,Outpoint_dpoutpoints,Outpoint_epdatagrough;/*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 filen);exit(0);for(i=0;idatagrough;i+)for(j=0;jinpoints;j+)fscanf(fp1,%lf,&InpointDataij); fclose(fp1);if(fp2=fopen(D:data训练输出.txt,r)=NULL)printf(2can not open the filen);exit(0);for(i=0;idatagrough;i+)for(j=0;joutpoints;j+)fscanf(fp2,%lf,&OutpointDataij); fclose(fp2);/*/*归一化*/void unitary()int i,j;int k=0;for(j=0;jinpoints;j+) /找出每列的最大、最小值存放在数组InpointData_MAXj、InpointData_MINj中 InpointData_MAXj=InpointData0j; InpointData_MINj=InpointData0j; for(i=0;idatagrough;i+) if(InpointData_MAXjInpointDataij) InpointData_MINj=InpointDataij;for(j=0;joutpoints;j+) /找出每列的最大、最小值存放在数组OutpointData_MAXj、OutpointData_MINj中 OutpointData_MAXj=OutpointData0j; OutpointData_MINj=OutpointData0j; for(i=0;idatagrough;i+) if(OutpointData_MAXjOutpointDataij) OutpointData_MINj=OutpointDataij;/*将数据归一处理,处理之后的数据全部在0，1之间*/for(j=0;jinpoints;j+) for(i=0;idatagrough;i+) if(InpointData_MAXj=0) InpointDataij=0; else InpointDataij=(InpointDataij-InpointData_MINj+A)/(InpointData_MAXj-InpointData_MINj+A);for(j=0;joutpoints;j+) for(i=0;idatagrough;i+) if(OutpointData_MAXj=0) OutpointDataij=0; else OutpointDataij=(OutpointDataij-OutpointData_MINj+A)/(OutpointData_MAXj-OutpointData_MINj+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;idefaultpoints;i+) /给输入层到隐层的连接权赋随机值LianJie_wij,这些值在0,1 for(j=0;jinpoints;j+) wij=(rand()*2.0/RAND_MAX-1)/2; dlta_wij=0;for(i=0;idefaultpoints;i+)limeni=(rand()*2.0/RAND_MAX-1)/2; dlta_limeni=0;for(i=0;ioutpoints;i+) /给隐层到输出层的连接权赋初值for(j=0;jdefaultpoints;j+) vij=(rand()*2.0/RAND_MAX-1)/2; dlta_vij=0;/*求单样本的计算输出误差*/void out_sub1(int t)int i,j;double defaultInpointdefaultpoints;double Outpoint_youtpoints; Outpoint_ept=0;for(i=0;idefaultpoints;i+)double sum=0;for(j=0;jinpoints;j+)sum+=wij*InpointDatatj;defaultInpointi=sum+limeni;defaultOutpointi=1/(a+b*exp(-1*c*defaultInpointi);/求Oifor(j=0;joutpoints;j+)/求YiOutpoint_yj=0;for(i=0;idefaultpoints;i+)Outpoint_yj+=vji*defaultOutpointi;Outpoint_dpj=OutpointDatatj-Outpoint_yj; Outpoint_ept+=Outpoint_dpj*Outpoint_dpj/2;/*反算权值*/void out_sub2(int t) int i,j,k; double s; for(i=0;idefaultpoints;i+) s=0; for(j=0;joutpoints;j+) dlta_vji=ALFA*dlta_vji+BETA*Outpoint_dpj*defaultOutpointi; / s+=vji*Outpoint_dpj; vji+=dlta_vji; dlta_limeni=ALFA*dlta_limeni+BETA*defaultOutpointi*(1-defaultOutpointi)*s;/ limeni+=dlta_limeni; for(k=0;kinpoints;k+) dlta_wik=ALFA*dlta_wik+BETA*defaultOutpointi*(1-defaultOutpointi)*s*InpointDatatk;/ wik=wik+dlta_wik; /*/void forecast()int i,j,t,k=0;double e,e1forecastdata=0; /训练误差double sss; double InputData_xforecastdatainpoints,tpforecastdataoutpoints;double defInpoint,defOutpointdefaultpoints,yforecastdataoutpoints;/yforecastdataoutpoints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 filen);exit(0);for(i=0;iforecastdata;i+)for(j=0;jinpoints;j+)fscanf(fp1,%lf,&InputData_xij); 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 filen);exit(0);for(i=0;iforecastdata;i+)for(j=0;joutpoints;j+)fscanf(fp3,%lf,&tpij); fclose(fp3); for(j=0;jinpoints;j+) / 检验数据归一化 for(i=0;iforecastdata;i+) if(InpointData_MAXj=0) InputData_xij=0; else InputData_xij=(InputData_xij-InpointData_MINj+A)/(InpointData_MAXj-InpointData_MINj+A); for(j=0;joutpoints;j+) for(i=0;iforecastdata;i+) if(OutpointData_MAXj=0) tpij=0; else tpij=(tpij-OutpointData_MINj+A)/(OutpointData_MAXj-OutpointData_MINj+A); do Initialization(); /初始化连接权值wij,limeni,vkik=0; do e=0;for(t=0;tdatagrough;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_ept; /计算输出误差k+;while(k0.1); sss=0; /中间参数 for(t=0;tforecastdata;t+) e1t=0; for(i=0;idefaultpoints;i+) double sum=0; for(j=0;jinpoints;j+) sum+=wij*InputData_xtj; defInpoint=sum+limeni; defOutpointi=1/(a+b*exp(-1*c*defInpoint); for(j=0;joutpoints;j+) ytj=0; for(i=0;i0.12);/*/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 filen);exit(0); for(i=0;idefaultp