BP神經(jīng)網(wǎng)絡(luò)算法的C語(yǔ)言實(shí)現(xiàn)代碼

BP神經(jīng)網(wǎng)絡(luò)算法的C語(yǔ)言實(shí)現(xiàn)代碼以下是一個(gè)BP神經(jīng)網(wǎng)絡(luò)的C語(yǔ)言實(shí)現(xiàn)代碼，代碼的詳細(xì)說(shuō)明可以幫助理解代碼邏輯：```c#include<stdio.h>#include<stdlib.h>#include<math.h>#defineINPUT_SIZE2#defineHIDDEN_SIZE2#defineOUTPUT_SIZE1#defineLEARNING_RATE0.1//定義神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)體typedefstructdoubleinput[INPUT_SIZE];doublehidden[HIDDEN_SIZE];doubleoutput[OUTPUT_SIZE];doubleweights_ih[INPUT_SIZE][HIDDEN_SIZE];doubleweights_ho[HIDDEN_SIZE][OUTPUT_SIZE];}NeuralNetwork;//激活函數(shù)doublesigmoid(doublex)return1/(1+exp(-x));//創(chuàng)建神經(jīng)網(wǎng)絡(luò)NeuralNetwork*create_neural_networNeuralNetwork*nn=(NeuralNetwork*)malloc(sizeof(NeuralNetwork));//初始化權(quán)重for(inti=0;i<INPUT_SIZE;i++)for(intj=0;j<HIDDEN_SIZE;j++)nn->weights_ih[i][j]=(double)rand(/RAND_MAX*2-1;}}for(inti=0;i<HIDDEN_SIZE;i++)for(intj=0;j<OUTPUT_SIZE;j++)nn->weights_ho[i][j]=(double)rand(/RAND_MAX*2-1;}}returnnn;//前向傳播voidforward(NeuralNetwork*nn)//計(jì)算隱藏層輸出for(inti=0;i<HIDDEN_SIZE;i++)doublesum=0;for(intj=0;j<INPUT_SIZE;j++)sum+=nn->input[j]*nn->weights_ih[j][i];}nn->hidden[i]=sigmoid(sum);}//計(jì)算輸出層輸出for(inti=0;i<OUTPUT_SIZE;i++)doublesum=0;for(intj=0;j<HIDDEN_SIZE;j++)sum+=nn->hidden[j]*nn->weights_ho[j][i];}nn->output[i]=sigmoid(sum);}voidbackpropagation(NeuralNetwork*nn,doubletarget)//計(jì)算輸出層誤差doubleoutput_error[OUTPUT_SIZE];for(inti=0;i<OUTPUT_SIZE;i++)doubledelta=target-nn->output[i];output_error[i]=nn->output[i]*(1-nn->output[i])*delta;}//更新隱藏層到輸出層權(quán)重for(inti=0;i<HIDDEN_SIZE;i++)for(intj=0;j<OUTPUT_SIZE;j++)nn->weights_ho[i][j]+=LEARNING_RATE*nn->hidden[i]*output_error[j];}}//計(jì)算隱藏層誤差doublehidden_error[HIDDEN_SIZE];for(inti=0;i<HIDDEN_SIZE;i++)doubledelta=0;for(intj=0;j<OUTPUT_SIZE;j++)delta+=output_error[j]*nn->weights_ho[i][j];}hidden_error[i]=nn->hidden[i]*(1-nn->hidden[i])*delta;}//更新輸入層到隱藏層權(quán)重for(inti=0;i<INPUT_SIZE;i++)for(intj=0;j<HIDDEN_SIZE;j++)nn->weights_ih[i][j]+=LEARNING_RATE*nn->input[i]*hidden_error[j];}}voidtrain(NeuralNetwork*nn,doubleinput[][2],doubletarget[],intnum_examples)intiteration=0;while(iteration<MAX_ITERATIONS)doubleerror=0;for(inti=0;i<num_examples;i++)for(intj=0;j<INPUT_SIZE;j++)nn->input[j]=input[i][j];}forward(nn);backpropagation(nn,target[i]);error+=fabs(target[i]-nn->output[0]);}//判斷誤差是否已達(dá)到允許范圍if(error<0.01)break;}iteration++;}if(iteration==MAX_ITERATIONS)printf("Trainingfailed!Error:%.8lf\n",error);}voidpredict(NeuralNetwork*nn,doubleinput[])for(inti=0;i<INPUT_SIZE;i++)nn->input[i]=input[i];}forward(nn);printf("Prediction:%.8lf\n",nn->output[0]);intmaiNeuralNetwork*nn=create_neural_network(;doubleinput[4][2]={0,0},{0,1},{1,0},{1,1}};doubletarget[4]=0,1,1,0};train(nn,input,target,4);predict(nn,input[0]);predict(nn,input[1]);predict(nn,input[2]);predict(nn,input[3]);free(nn);return0;```以上代碼實(shí)現(xiàn)了一個(gè)簡(jiǎn)單的BP神經(jīng)網(wǎng)絡(luò)，該神經(jīng)網(wǎng)絡(luò)包含一個(gè)輸入層、一個(gè)隱藏層和一個(gè)輸出層。代碼中使用的激活函數(shù)為sigmoid函數(shù)，使用均方誤差作為損失函數(shù)進(jìn)行訓(xùn)練。神經(jīng)網(wǎng)絡(luò)通過(guò)`create_neural_network`函數(shù)創(chuàng)建，并初始化權(quán)重。`forward`函數(shù)實(shí)現(xiàn)了前向傳播的過(guò)程，`backpropagation`函數(shù)實(shí)現(xiàn)了反向傳播的過(guò)程。`train`函數(shù)用于訓(xùn)練神經(jīng)網(wǎng)絡(luò)，根據(jù)已知的輸入和目標(biāo)輸出進(jìn)行迭代更新權(quán)重，直至誤差達(dá)到允許范圍或達(dá)到最大迭代次數(shù)。`predict`函數(shù)用于使用已訓(xùn)練好的神經(jīng)