c++BP算法源程序3.doc

下面是BPNET.CPP源文件#=BpNet.cpp : implementation file=/人工神經(jīng)網(wǎng)絡(luò)BP算法/1、動態(tài)改變學習速率/2、加入動量項/3、運用了Matcom4.5的矩陣運算庫（可免費下載,頭文件matlib.h)，/ 方便矩陣運算,當然，也可自己寫矩陣類/4、可暫停運算/5、可將網(wǎng)絡(luò)以文件的形式保存、恢復/作者：同濟大學材料學院 張純禹/email:chunyu_79/QQ:53806186/歡迎不斷改進!歡迎討論其他實用的算法!/#include BpNet.h#ifdef _DEBUG#define new DEBUG_NEW#undef THIS_FILEstatic char THIS_FILE = _FILE_;#endif/ CBpNetIMPLEMENT_SERIAL( CBpNet, CObject, 1 )CBpNet:CBpNet()initM(MATCOM_VERSION);/啟用矩陣運算庫CBpNet:CBpNet()exitM();delete this;/ CBpNet message handlers/創(chuàng)建新網(wǎng)絡(luò)void CBpNet:Create(Mm mInputData, Mm mTarget, int iInput, int iHidden, int iOutput) 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;/創(chuàng)建計算用的單個樣本矩陣mInput=zeros(1,this-iInput);mHidden=zeros(1,this-iHidden);mOutput=zeros(1,this-iOutput);/創(chuàng)建權(quán)重矩陣,并賦初值mWeighti=zeros(this-iInput,this-iHidden);mWeighto=zeros(this-iHidden,this-iOutput); /賦初值for(i=1;iiInput;i+) for(j=1;jiHidden;j+) mWeighti.r(i,j)=randab(-1.0,1.0);for(i=1;iiHidden;i+) for(j=1;jiOutput;j+) mWeighto.r(i,j)=randab(-1.0,1.0);/創(chuàng)建闕值矩陣，并賦值mThresholdi=zeros(1,this-iHidden);for(i=1;iiHidden;i+) mThresholdi.r(i)=randab(-1.0,1.0);mThresholdo=zeros(1,this-iOutput);for(i=1;iiOutput;i+) mThresholdo.r(i)=randab(-1.0,1.0);/創(chuàng)建權(quán)重變化矩陣mChangei=zeros(this-iInput,this-iHidden);mChangeo=zeros(this-iHidden,this-iOutput);mInputNormFactor=zeros(iInput,2);mTargetNormFactor=zeros(iOutput,2);/誤差矩陣 mOutputDeltas=zeros(iOutput); mHiddenDeltas=zeros(iHidden);/學習速率賦值dblLearnRate1=0.5;dblLearnRate2=0.5;dblMomentumFactor=0.95;m_isOK=false;m_IsStop=false;dblMse=1.0e-6;/誤差限dblError=1.0;lEpochs=0;/根據(jù)已有的網(wǎng)絡(luò)進行預測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,輸入數(shù)據(jù)變量個數(shù)錯誤!,輸入數(shù)據(jù)變量個數(shù)錯誤!,MB_OK);return mResult;mResult=zeros(data.rows(),iOutput); /正規(guī)化數(shù)據(jù)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);/計算 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+) /輸入層數(shù)據(jù) for(i=1;i=iInput;i+) mInputdata.r(i)=data.r(iSample,i); /隱層數(shù)據(jù) 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); /輸出數(shù)據(jù) 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); /轉(zhuǎn)換 for(j=1;jdblMse&!m_IsStop) dblTotal=0.0; for(iSample=1;iSample1.04)/動態(tài)改變學習速率 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);/這樣可以消除屏閃和假死機if(dblError=dblMse)m_isOK=true;elsem_isOK=false;void CBpNet:stop()m_IsStop=true;double CBpNet:randab(double a, double b) /注意，如果應(yīng)用矩陣庫，頭文件matlib.h對rand()函數(shù)重新定義，只產(chǎn)生（0，1）/之間的隨機數(shù)return(b-a)*rand()+a);/將數(shù)據(jù)轉(zhuǎn)化到(0,1)區(qū)間void CBpNet:normalize()int i,j;/輸入數(shù)據(jù)范圍mInputNormFactor=scope(mSampleInput);/目標數(shù)據(jù)范圍mTargetNormFactor=scope(mSampleTarget);for(i=1;i=mSampleInput.rows();i+) for(j=1;j=mSampleInput.cols();j+) mSampleInput.r(i,j)=(mSampleInput.r(i,j)-mInputNormFactor.r(j,1)/(mInputNormFactor.r(j,2)-mInputNormFactor.r(j,1);for(i=1;i=mSampleTarget.rows();i+) for(j=1;j=mSampleTarget.cols();j+) mSampleTarget.r(i,j)=(mSampleTarget.r(i,j)-mTargetNormFactor.r(j,1)/(mTargetNormFactor.r(j,2)-mTargetNormFactor.r(j,1);/前向計算 void CBpNet:forward(int iSample)/根據(jù)第iSample個樣本，前向計算 if(iSamplemSampleInput.rows() MessageBox(NULL,無此樣本數(shù)據(jù):索引出界!,無此樣本數(shù)據(jù):索引出界!,MB_OK); return; int i,j; double sum=0.0; /輸入層數(shù)據(jù) for(i=1;i=iInput;i+) mInput.r(i)=mSampleInput.r(iSample,i); /隱層數(shù)據(jù) for(j=1;j=iHidden;j+) sum=0.0; for(i=1;i=iInput;i+) sum+=mInput.r(i)*mWeighti.r(i,