BP算法的C语言实现.doc

终于搞定了，输出结果完全正确，运行训练次数竟然有几万次！汗！（适合普通BP算法和改进型BP带缓冲项算法）/训练样本大概是这些(每组前三个为in样本数据 后面为out结果)：/0 0 0 0.9/0 0 1 0.1/0 1 0 0.1/0 1 1 0.9/1 0 0 0.1/1 0 1 0.9/1 1 0 0.9/1 1 1 0.1测试样本可以自己试试这些数据/0 0.1 0.9（期望：0.1）/0.9 0.9 0.1（期望0.9）-分割线-#include stdlib.h#include math.h#include conio.h#include stdio.h#include time.h#define N 8 /*学习样本个数(测试样本个数)*/#define IN 3 /*输入层神经元数目*/#define HN 2 /*隐层神经元数目*/#define ON 1 /*输出层神经元数目*/float PIN; /*单个样本输入数据*/float TON; /*单个样本教师数据*/float WHNIN; /*输入层至隐层权值*/float VONHN; /*隐层至输出层权值*/float XHN; /*隐层的输入*/float YON; /*输出层的输入*/float HHN; /*隐层的输出*/float OON; /*输出层的输出*/float YU_HNHN; /*隐层的阈值*/float YU_ONON; /*输出层的阈值*/float err_mN; /*第m个样本的总误差*/float a; /*输出层至隐层学习效率*/float b; /*隐层至输入层学习效率*/float alpha; /*/动量因子，改进型bp算法使用*/float d_errON;/*k*/float e_errHN;/*j*/FILE *fp;/*定义一个放学习样本的结构*/struct float inputIN;float teachON; Study_DataN;/*定义一个放测试样本的结构*/struct float inputIN;float expectON; Test_DataN;/*改进型bp算法用来保存每次计算的权值*/float old_WHNIN;float old_VONHN;int Start_Show()clrscr();printf(n * n);printf( * Welcome to use *n);printf( * this program of *n);printf( * calculating the BP *n);printf( * model! *n);printf( * Happy every day! *n);printf( *n);printf(nnBefore starting,please read the follows carefully:nn);printf( The program of BP can study itself for no more than 200000 times.nAnd surpassing the number,the program will be ended by itself innpreventing running infinitely because of error!n);printf(nnn);printf(Now press any key to start.n);getch();clrscr();int End_Show()printf(nn-n);printf(The program has reached the end successfully!nnPress any key to exit!nn);printf(n *n);printf( * This is the end *n);printf( * of the program which*n);printf( * can calculate the BP*n);printf( * model! *n);printf( *n);printf( * Thanks for using! *n);printf( * Happy every day! *n);printf( *n);getch();exit(0);/*读取训练样本*/GetTrainingData()int i,j,m;float datr;if(fp=fopen(sample.txt,r)=NULL) printf(Cannot open file strike any key exit!); getch(); exit(1); for(i=0;iIN-1) Study_Datai.teachj-IN=datr; else Study_Datai.inputj=datr;/*printf(nthe Study_Data%d.input%d=%fn %f,i,j,Study_Datai.inputj,datr);getch();*/*use to check the loaded training datas*/ j+; fclose(fp);printf(nThere are %d sample datas that have been loaded successfully!n,N*(IN+ON);printf(nShow the data which has been loaded as follows:n);for(m=0;mN;m+) for(i=0;iIN;i+) printf(Study_Data%d.input%d=%f ,m,i,Study_Datam.inputi); for(j=0;jON;j+) printf(Study_Data%d.teach%d=%f ,m,j,Study_Datam.teachj); printf(nnnPress any key to begin Study.);getch();clrscr();return 1;/*初始化权、阈值子程序*/initial()int i;int ii;int j;int jj;int k;int kk;printf(nRandsom Weight value and Bias value as follow:n);srand(time(NULL);/*随机函数种子*/printf(nWeight Value:n);for(i=0;iHN;i+) for(j=0;jIN;j+) Wij=(float)(rand()/32767.0)*2-1)/2);/*初始化输入层到隐层的权值，随机模拟0.5-0.5 */ printf(nw%d%d=%f,i,j,Wij); for(ii=0;iiON;ii+) for(jj=0;jjHN;jj+) Viijj= (float)(rand()/32767.0)*2-1)/2);/*初始化隐层到输出层的权值，随机模拟0.5-0.5 */ printf(nV%d%d=%f,ii,jj,Viijj); printf(nnBias Value:n);for(k=0;kHN;k+) YU_HNk = 1.0;/*隐层阈值初始化 ,-0.01 0.01 之间*/ printf(nYU_HN%d=%f,k,YU_HNk); for(kk=0;kkON;kk+) YU_ONkk = 1.0;/*输出层阈值初始化 ,-0.01 0.01 之间*/ printf(nYU_ON%d=%fn,kk,YU_ONkk); printf(nnnnnPress any key to start culculating.:n);getch();clrscr();printf(Please wait.);return 1;/*第m个学习样本输入子程序*/input_P(int m) int i,j; for(i=0;iIN;i+) Pi=Study_Datam.inputi; return 1;/*第m个样本教师信号子程序*/input_T(int m)int k;for(k=0;kON;k+) Tk=Study_Datam.teachk;return 1;/*求净输入，输出*/IN_OUT()float sigma1,sigma2;int i,j,ii,jj;for(i=0;iHN;i+) sigma1=0.0; for(j=0;jIN;j+) sigma1+=Wij*Pj;/*求隐层内积*/ Xi=sigma1+YU_HNi; Hi=1.0/(1.0+exp(-Xi); for(ii=0;iiON;ii+) sigma2=0.0; for(jj=0;jjHN;jj+) sigma2+=Viijj*Hjj; Yii=sigma2+YU_ONii; Oii=1.0/(1.0+exp(-Yii); return 1;/*误差分析*/*k*/int Err_O_H(int m)int k;float abs_errON;float sqr_err=0.0;for (k=0;kON;k+) abs_errk=Tk-Ok; sqr_err+=(abs_errk)*(abs_errk); d_errk=abs_errk*Ok*(1.0-Ok); err_mm=sqr_err/2; return 1;/*j*/int Err_H_I()int j,k;float sigma;for(j=0;jHN;j+) sigma=0.0; for(k=0;kON;k+) sigma+=d_errk*Vkj; e_errj=sigma*Hj*(1-Hj); return 1;/*总误差*/float Err_Sum()int m;float total_err=0.0;for(m=0;mN;m+) total_err+=err_mm;return total_err;/*新旧权值更新量交替-改进型Bp算法*/saveWV()int i;int ii;int j;int jj;for(i=0;iHN;i+) for(j=0;jIN;j+) old_Wij =b*e_erri*Pj; for(ii=0;iiON;ii+) for(jj=0;jjHN;jj+) old_Viijj =a*d_errii*Hjj; return 1;/*更新权值，阈值*/*输出层*/int Delta_O_H(int n,int study)int k,j;if(n=1)|(study=1) for (k=0;kON;k+) for (j=0;jHN;j+) Vkj=Vkj+a*d_errk*Hj; YU_ONk+=a*d_errk; else for (k=0;kON;k+) for (j=0;jHN;j+) Vkj=Vkj+(1.0-alpha)*a*d_errk*Hj+alpha*old_Vkj; YU_ONk+=a*d_errk; return 1;/*隐层*/Delta_H_I(int n,int study)int i,j;if(n=1)|(study=1) for (j=0;jHN;j+) for (i=0;iIN;i+) Wji=Wji+b*e_errj*Pi; YU_HNj+=b*e_errj; else for(j=0;jHN;j+) for(i=0;iIN;i+) Wji=Wji+(1.0-alpha)*b*e_errj*Pi+alpha*old_Wji; YU_HNj+=b*e_errj; return 1;/*保存更新*/void savequan() int i,j,k; int ii,jj,kk; /*save weight*/ if(fp=fopen(weight.txt,a)=NULL) printf(Cannot open file strike any key exit!); getch(); exit(1); for(i=0;iHN;i+) for(j=0;jIN;j+) fprintf(fp,W%d%d=%fn,i,j,Wij); fprintf(fp,n); for(ii=0;iiON;ii+) for(jj=0;jjHN;jj+) fprintf(fp,V%d%d=%fn,ii,jj,Viijj); fclose(fp); printf(nThe result of weight.txt(quanzhi) has been saved successfully!); /*save limit*/ if(fp=fopen(limit.txt,a)=NULL) printf(Cannot open file strike any key exit!); getch(); exit(1); for(k=0;kON;k+) fprintf(fp,YU_ON%d=%fn,k,YU_ONk); for(kk=0;kkHN;kk+) fprintf(fp,YU_HN%d=%fn,kk,YU_HNkk); fclose(fp); printf(nThe result of limit.txt(yuzhi) has been saved successfully!nnnnnPress any key to Test.); getch(); clrscr();/*读取测试样本*/GetTestData()int i,j,m;float datr;if(fp=fopen(test.txt,r)=NULL) printf(Cannot open file strike any key exit!); getch(); exit(1); for(i=0;iIN-1) Test_Datai.expectj-IN=datr; else Test_Datai.inputj=datr; j+; fclose(fp);printf(nThere are %d test datas that have been loaded successfully!n,N*(IN+ON);printf(nShow the data which has been loaded as follows:n);for(m=0;mN;m+) for(i=0;iIN;i+) printf(Test_Data%d.input%d=%f ,m,i,Test_Datam.inputi); for(j=0;jON;j+) printf(Test_Data%d.expec%d=%f ,m,j,Test_Datam.expectj); printf(nnnnPress any key to culculating.);getch();clrscr();return 1;/*样本测试及结果*/Test()int i,j,k,m;float net1HN,net2ON,H_netHN,O_netON;for(m=0;mN;m+) for(i=0;iHN;i+) net1i=0.0; for(j=0;jIN;j+) net1i+=Test_Datam.inputj*Wij; net1i=net1i+YU_HNi; H_neti=1.0/(1.0+exp(-net1i); for(k=0;kON;k+) net2k=0.0; for(j=0;jHN;j+) net2k+=H_netj*Vkj; net2k=net2k+YU_ONk; O_netk=1.0/(1.0+exp(-net2k); printf(nTest result%d=%fn,m,O_netk); /*直接输入测试*/OnlineSet()char s;printf(nnnnIf you want to test the data you inputted now,press Y(or other button except Q);nOr press Q to quit Test!n);while(1) s=getch(); switch(s) case Q: clrscr(); End_Show(); default: Online_Test();break; printf(nTo continue testing press Y(or other button except Q);nOr press Q to quit Test!n); getch();Online_Test()int i,j,k;float datr,TSIN;float net1HN,net2ON,H_netHN,O_netON;printf(nPlease Input %d test datas with a space between each two datas:n,IN);for(i=0;iIN;i+) scanf(%f,&datr); TSi=datr; for(i=0;iHN;i+) net1i=0.0; for(j=0;jIN;j+) net1i+=TSj*Wij; net1i=net1i+YU_HNi; H_neti