BP神經(jīng)網(wǎng)絡算法的C語言實現(xiàn)代碼

1、BP神經(jīng)網(wǎng)絡算法的C語言實現(xiàn)代碼/BP神經(jīng)網(wǎng)絡算法,c語言版本/VS2010下，無語法錯誤，可直接運行添加了簡單注釋歡迎學習交流#include<stdlib.LayerNum>#include<math.LayerNum>#include<stdio.LayerNum>#include<time.LayerNum>#define N_Out 2輸出向量維數(shù)#defineN_In3隔入向量維數(shù)#defineN_Sample6樣本數(shù)量/BP人工神經(jīng)網(wǎng)絡typedefstruct(intLayerNum;中間層數(shù)量doublevN_In50;中間層權

2、矩陣i,中間層節(jié)點最大數(shù)量為50doublew50N_Out;輸出層權矩陣doubleStudyRate;學習率doubleAccuracy;精度控制參數(shù)intMaxLoop;最大循環(huán)次數(shù)BPNet;/Sigmoid函數(shù)doublefnet(doublenet)(returnl/(l+exp(-net);)初始化intInitBpNet(BPNet*BP);訓練BP網(wǎng)絡，樣本為x,理想輸出為yintTrainBpNet(BPNet*BP,doublexN_SampleNin,intyNSampleN_Out);使用BP網(wǎng)絡intUseBpNet(BPNet*BP);主函數(shù)intmain。訓練樣

3、本doublexNSampleNin=0.8,0.5,0,0.9,0.7,03,l,0.8,0.5,0,0.2,0.3,02,0.l,l.3,0.2,0.7,0.8;理想輸出intyNSampleN_Out=0,1,0,1,0,1,1,1,1,0,1,0)；BPNetBP;InitBpNet(&BP);初始化BP網(wǎng)絡結構TrainBpNet(&BP,x,y);訓練BP神經(jīng)網(wǎng)絡UseBpNet(&BP);測試BP神經(jīng)網(wǎng)絡return1;)使用BP網(wǎng)絡intUseBpNet(BPNet*BP)(doubleInputN_In;doubleOut150;doubleOut2N

4、_Out;/Outl為中間層輸出Qut2為輸出層輸出持續(xù)執(zhí)行，除非中斷程序while(1)printfC請輸入3個數(shù)：nn);intij;for(i=0;i<N_In;i+)scanf_s(n%r&Inputi);doubleTmp;for(i=0;i<(*BP).LayerNum;i+)|Tmp=0;for(j=0;j<N_In;j+)Tmp+=Inputs*Outli=fnet(Tmp);)for(i=0;i<N_Out;i+)(Tmp=0;for(j=0;j<(*BP).LayerNum;j+)Tmp+=Outlj*(*BP).w皿i;Out2i=f

5、net(Tmp);)printfC結果：n);for(i=0;i<N_Out;i+)printf(H%.3fH,Out2i);printf(nnn);)return1;訓練BP網(wǎng)絡，樣本為x,理想輸出為yintTrainBpNet(BPNet*BP,doublexN_SampleN_In,intyN_SampleN_Out)doublef=(*BP).Accuracy;精度學控制參數(shù)doublea=(BP).StudyRate;習率intLayerNum=(*BP).LayerNum;中間層節(jié)點數(shù)doublevN_In50,w50N_Out;權矩陣doubleChgH50,ChgON_O

6、ut;/修改量矩陣doubleOutl50,Out2N_Out;中間層和輸出層輸出量int MaxLoop(*BP).MaxLoop;最大循環(huán)次數(shù)intn;doubleTmp;for(i=0;i<N_In;i+)/復制結構體中的權矩陣for(j=0;j<LayerNum;j+)viU=(*BP).viU；for(i=0;i<LayerNum;i+)for(j=0;j<N_Out;j+)Wij=(*BP).wij;doublee=f+1;對每個樣本訓練網(wǎng)絡for(n=0;e>f&&n<MaxLoop;n+)(e=0;for(i=0;i<N

7、_Sample;i+)計算中間層輸出向量for(k=0;k<LayerNum;k+)Tmp=0;for(j=0;j<N_In;j+)Tmp=Tmp+xij*vjk;Outlk=fnet(Tmp);)計算輸出層輸出向量for(k=0;k<N_Out;k+)Tmp=0;for(j=0;j<LayerNum;j+)Tmp=Tmp+Outlj*wjk;Out2k=fnet(Tmp);)計算輸出層的權修改量for(j=0;j<N_Out;j+)ChgOj=Out2j*(1-Out2j)*(yiU-Out2U)；計算輸出誤差for(j=0;j<N_Out;j+)e=e+

8、(yiU-Out2j)*(yiU-Out2UD;計算中間層權修改量for(j=0;j<LayerNum;j+)Tmp=0;for(k=0;k<N_Out;k+)Tmp=Tmp+wjk*ChgOk;ChgHj=Tmp*Outlj*(1-Outiui)；)修改輸出層權矩陣for(j=0;j<LayerNum;j+)for(k=0;k<N_Out;k+)wjk=wjk+a*Outlj*ChgOk;for(j=0;j<N_In;j+)for(k=0;k<LayerNum;k+)vUk=v皿k+a*xiU*ChgHk;)if(n%10=0)printf("誤

9、差：e);printf("總共循環(huán)次數(shù)：%dnfn);printf("調整后的中間層權矩陣：nn);for(i=0;i<N_In;i+)for(j=0;j<LayerNum;j+)printf(n%fvij);printf(nnn);)printfC調整后的輸出層權矩陣：n");for(i=0;i<LayerNum;i+)for(j=0;j<N_Out;j+)printf(n%fwij);printf(nnn);)把結果復制回結構體for(i=0;i<N_In;i+)for(j=0;j<LayerNum;j+)(*BP).vij

10、=vij;for(i=0;i<LayerNum;i+)for(j=0;j<N_Out;j+)(*BP).wij=wij;printfBP網(wǎng)絡訓練結束！nn);return1;初始化intInitBpNet(BPNet*BP)printf請輸入中間層節(jié)點數(shù)，最大數(shù)為100：W'')；scanf_s(M%d'&(*BP)>LayerNum);printf(1，請輸入學習率:n“);scanf_s(n%lf&(BP).StudyRate);/(BP).StudyRate為double型數(shù)據(jù)，所以必須是Ifprintf（"請輸入精度控制參數(shù)：nn）;scanf_s(M%IF&(*BP)>Accuracy);printf請輸入最大循環(huán)次數(shù)：n"）;scanf_s(n%dM,&(*BP).MaxLoop);intij;srand(unsigned)time(NULL);for(i=0;i<N_In;