神经网络bp.txt

是BP神经网络的改进源程序

#if !defined(AFX_BPNET_H__7ACF7725_EE66_11D6_AAF0_00E04F29491B__INCLUDED_) 
#define AFX_BPNET_H__7ACF7725_EE66_11D6_AAF0_00E04F29491B__INCLUDED_ 

#if _MSC_VER > 1000 
#pragma once 
#endif // _MSC_VER > 1000 
// BpNet.h : header file 
// 

///////////////////////////////////////////////////////////////////////////// 

// CBpNet window 
#include<matlib.h> 
class CBpNet : public CObject 
{ 
// Construction 
public: 
CBpNet(); 

// Attributes 
public: 

// Operations 
public: 

// Overrides 
// ClassWizard generated virtual function overrides 
//{{AFX_VIRTUAL(CBpNet) 
//}}AFX_VIRTUAL 

// Implementation 
public: 
void Serialize( CArchive& ar ); 
void display(Mm data); 
Mm scope(Mm mData); 
long lEpochs; 
double dblMse; 
double dblError; 

double randab(double a,double b); 
void stop(); 
void learn(); 
bool SaveBpNet(CString &strNetName); 
void LoadBpNet(CString &strNetName); 
Mm simulate(Mm mData); 
void Create(Mm mInputData,Mm mTarget,int iInput,int iHidden,int iOutput); 
virtual ~CBpNet(); 

// Generated message map functions 
protected: 
//{{AFX_MSG(CBpNet) 
// NOTE - the ClassWizard will add and remove member functions here. 
//}}AFX_MSG 

DECLARE_SERIAL(CBpNet) 

public: 
bool m_isOK; 
void LoadPattern(Mm mIn,Mm mOut); 
int iHidden;//隐层神经元个数 
int iInput;//输入个数 
int iOutput;//输出个数 
protected: 

Mm mInput;//单个样本输入数据 
Mm mSampleInput;//全体样本输入数据 
Mm mSampleTarget;//全体目标数据 
    Mm mHidden;//计算得到的隐层数据 
Mm mOutput;//计算输出 
Mm mWeighti;//输入－隐层权重 
Mm mWeighto;//隐层－输出权重 
Mm mChangei;//输入－隐层权重变化 
Mm mChangeo;//隐层－输出权重变化 
public: 
Mm mInputNormFactor;//正规化因子，iInputx2 
Mm mTargetNormFactor;//输出正规化因子,iOutputx2 
protected: 
Mm mThresholdi;//阙值 
Mm mThresholdo; 
Mm mOutputDeltas;//误差 
Mm mHiddenDeltas; 
protected: 
bool m_IsStop; 
double dblMomentumFactor; 
double dblLearnRate1; 
double dblLearnRate2; 
void backward(int iSample); 
void forward(int iSample); 
void normalize();//将输入输出样本数据正规化处理 

private: 
double dblErr; 
}; 

///////////////////////////////////////////////////////////////////////////// 


//{{AFX_INSERT_LOCATION}} 
// Microsoft Visual C++ will insert additional declarations immediately before
 the previous line. 

#endif // !defined(AFX_BPNET_H__7ACF7725_EE66_11D6_AAF0_00E04F29491B__INCLUDED
_) 






// BpNet.cpp : implementation file 
//////////////////////////////////////////////////////////////////// 
/////////////////人工神经网络BP算法///////////////////////////////// 
//1、动态改变学习速率 
//2、加入动量项 
//3、运用了Matcom4.5的矩阵运算库（可免费下载,头文件matlib.h)， 
//   方便矩阵运算,当然，也可自己写矩阵类 
//4、可暂停运算 
//5、可将网络以文件的形式保存、恢复 
///////////////作者：同济大学材料学院   张纯禹////////////////////// 
///////////////email:[e-mail]////////////////////////// 
///////////////QQ:53806186////////////////////////////////////////// 
///////////////欢迎不断改进!欢迎讨论其他实用的算法!///////////////// 

#include "BpNet.h" 

#ifdef _DEBUG 
#define new DEBUG_NEW 
#undef THIS_FILE 
static char THIS_FILE[] = __FILE__; 
#endif 

///////////////////////////////////////////////////////////////////////////// 

// CBpNet 
IMPLEMENT_SERIAL( CBpNet, CObject, 1 ) 

CBpNet::CBpNet() 
{initM(MATCOM_VERSION);//启用矩阵运算库 
} 

CBpNet::~CBpNet() 
{exitM(); 
 delete this; 
} 




///////////////////////////////////////////////////////////////////////////// 

// CBpNet message handlers 
//创建新网络 
void CBpNet::Create(Mm mInputData, Mm mTarget, int iInput, int iHidden, int iO
utput) 
{ int i,j; 
  mSampleInput=zeros(mInput.rows(),mInput.cols()); 
  mSampleTarget=zeros(mTarget.rows(),mTarget.cols());   
  mSampleInput=mInputData; 
  mSampleTarget=mTarget; 
  this->iInput=iInput; 
  this->iHidden=iHidden; 
  this->iOutput=iOutput; 
  //创建计算用的单个样本矩阵 
  mInput=zeros(1,this->iInput); 
  mHidden=zeros(1,this->iHidden); 
  mOutput=zeros(1,this->iOutput); 
  //创建权重矩阵,并赋初值 
  mWeighti=zeros(this->iInput,this->iHidden); 
  mWeighto=zeros(this->iHidden,this->iOutput);  
  //赋初值 
  for(i=1;i<=this->iInput;i++) 
  for(j=1;j<=this->iHidden;j++) 
  mWeighti.r(i,j)=randab(-1.0,1.0); 
  for(i=1;i<=this->iHidden;i++) 
  for(j=1;j<=this->iOutput;j++) 
  mWeighto.r(i,j)=randab(-1.0,1.0); 
   
  //创建阙值矩阵，并赋值 
  mThresholdi=zeros(1,this->iHidden); 
  for(i=1;i<=this->iHidden;i++) 
  mThresholdi.r(i)=randab(-1.0,1.0); 
  mThresholdo=zeros(1,this->iOutput); 
  for(i=1;i<=this->iOutput;i++) 
  mThresholdo.r(i)=randab(-1.0,1.0); 
  //创建权重变化矩阵 
  mChangei=zeros(this->iInput,this->iHidden); 
  mChangeo=zeros(this->iHidden,this->iOutput); 
   
  mInputNormFactor=zeros(iInput,2); 
  mTargetNormFactor=zeros(iOutput,2); 
  //误差矩阵 
   mOutputDeltas=zeros(iOutput); 
   mHiddenDeltas=zeros(iHidden); 
  //学习速率赋值 
  dblLearnRate1=0.5; 
  dblLearnRate2=0.5; 
  dblMomentumFactor=0.95; 
   
  m_isOK=false; 
  m_IsStop=false; 
  dblMse=1.0e-6;//误差限 
  dblError=1.0; 
  lEpochs=0; 

} 
//根据已有的网络进行预测 
Mm CBpNet::simulate(Mm mData) 
{int i,j; 
 Mm mResult; 
 Mm data=zeros(mData.rows(),mData.cols()); 
 data=mData; 
 if(mData.cols()!=iInput) 
 {::MessageBox(NULL,"输入数据变量个数错误!","输入数据变量个数错误!",MB_OK); 
  return mResult; 
 } 
 mResult=zeros(data.rows(),iOutput);  
 //正规化数据 
 for(i=1;i<=data.rows();i++) 
 for(j=1;j<=data.cols();j++)  
         data.r(i,j)=(data.r(i,j)-mInputNormFactor.r(j,1))/(mInputNormFactor.r
(j,2)-mInputNormFactor.r(j,1)); 
 //计算 
 int iSample; 
 Mm mInputdata,mHiddendata,mOutputdata; 
 mInputdata=zeros(1,iInput); 
 mHiddendata=zeros(1,iHidden); 
 mOutputdata=zeros(1,iOutput); 
 double sum=0.0; 
   for(iSample=1;iSample<=data.rows();iSample++){  
 //输入层数据 
    for(i=1;i<=iInput;i++) 
  mInputdata.r(i)=data.r(iSample,i); 
 //隐层数据 
for(j=1;j<=iHidden;j++){ 
  sum=0.0; 
 for(i=1;i<=iInput;i++) 
sum+=mInputdata.r(i)*mWeighti.r(i,j); 
 sum-=mThresholdi.r(j);  
 mHiddendata.r(j)=1.0/(1.0+exp(-sum)); 
} 
     
  //输出数据 
for(j=1;j<=iOutput;j++){ 
sum=0.0; 
for(i=1;i<=iHidden;i++) 
sum+=mHiddendata.r(i)*mWeighto.r(i,j); 
sum-=mThresholdo.r(j);  
mOutputdata.r(j)=1.0/(1.0+exp(-sum)); 
} 

//转换 
for(j=1;j<=iOutput;j++) 
        mResult.r(iSample,j)=mOutputdata.r(j)*(mTargetNormFactor.r(j,2)-mTarge
tNormFactor.r(j,1))+mTargetNormFactor.r(j,1); 
 } 
   
 return (mResult); 
} 

void CBpNet::LoadBpNet(CString &strNetName) 
{CFile file; 
  
 if(file.Open(strNetName,CFile::modeRead)==0)    
 {MessageBox(NULL,"无法打开文件!","错误",MB_OK); 
  return; 
 } 
 else{ 
 CArchive myar(&file,CArchive::load); 
 Serialize(myar);  
 myar.Close(); 
 } 
 file.Close();  
} 

bool CBpNet::SaveBpNet(CString &strNetName) 
{CFile file; 
 if(strNetName.GetLength()==0) 
 return(false); 
 if(file.Open(strNetName,CFile::modeCreate|CFile::modeWrite)==0)    
 {MessageBox(NULL,"无法创建文件!","错误",MB_OK); 
  return(false); 
 } 
 else{ 
 CArchive myar(&file,CArchive::store); 
 Serialize(myar);  
 myar.Close(); 
 } 
 file.Close();  
 return(true); 
} 
//网络学习 
void CBpNet::learn() 
{ int iSample=1; 
  double dblTotal; 
  MSG msg; 
  if(m_IsStop) 
  m_IsStop=false; 
  //数据正规化处理 
  normalize(); 

  while(dblError>dblMse&&!m_IsStop){ 
   dblTotal=0.0; 
   for(iSample=1;iSample<=mSampleInput.rows();iSample++){ 
forward(iSample); 
    backward(iSample); 
dblTotal+=dblErr;//总误差 
  } 
   if(dblTotal/dblError>1.04){//动态改变学习速率 
   dblLearnRate1*=0.7; 
       dblLearnRate2*=0.7; 
   } 
   else{ 
       dblLearnRate1*=1.05; 
       dblLearnRate2*=1.05; 
   } 
   lEpochs++; 
   dblError=dblTotal; 

   ::PeekMessage(&msg,NULL,0,0,PM_REMOVE); 
   ::DispatchMessage(&msg); 
     msg.message=-1; 
   ::DispatchMessage(&msg);//这样可以消除屏闪和假死机 
  } 
if(dblError<=dblMse)