BP算法的实现概要

1、BP 算法的实现适合普通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#d

2、efine 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_HNH

3、N; /*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;fl

4、oat 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 touse*n);printf(*this programof*n);printf(*calculating the BP*n);printf(*model!*n);printf(*Happy everyday!*n);printf(*n);printf(nnBefore starting,please read the foll

5、ows 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 runninginfinitely because of error!n);printf(nnn);printf(Now press any key to start.n);getch();clrscr();int End_Show()printf(nn-n);p

6、rintf(Theprogram has reached the end successfully!nnPressany key to exit!nn);printf(n*n);printf(*This is theend*n);printf(* of the program which*n);printf(* can calculate the BP*n);printf(*model!*n);printf(*n);printf(*Thanks forusing!*n);printf(*Happy everyday!*n);printf(*n);getch();exit(0);/* 读取训练样

7、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;elseStudy_Datai.inputj=datr;/*printf(ntheStudy_Data%d.input%d=%fn %f,i,j,Study_Datai.inputj,datr);getch();*/ /*use to check

8、the loaded training datas*/j+;fclose(fp);printf(nThereare%d sample datasthathave been loadedsuccessfully!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%

9、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+)Wi

10、j=(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

11、 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.

12、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;/*Xi=sigma1+YU_HNi;Hi=1.0/(1.0+exp(-Xi);求隐层内积*/for(ii=0;iiON;ii+) sigma2=0.0;for(jj=0;jjHN

13、;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 sigm

14、a;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*P

15、j;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;elsefor (k=0;kON;k+)for (j=0;jHN;j+)Vkj=Vkj+(1.0-alpha)*a*d_errk*Hj+alpha*old_Vkj;YU

16、_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;elsefor(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

17、,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.

18、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(yuzh

19、i) has been savedsuccessfully!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;elseTest_Datai.inputj=datr;j+;fclose(fp);print

20、f(nThereare %d testdatasthathave been loadedsuccessfully!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 k

21、ey 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;

22、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 buttonexcept Q);nOr press Q to quit Test!n);while(1)s=getch();switch(s) case Q:clrscr();End_Show();default:Online_

23、Test();break;printf(nTocontinuetestingpressY(or otherbuttonexceptQ);nOrpressQ 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=1.0/