BP神经网络算法的C语言实现代码

1、bp神经网络算法的c语言实现代码/bp神经网络算法,c语言版本/vs2010下，无语法错误，可直接运行添加了简单注释欢迎学习交流#include #include #include #include #define n_out 2输出向量维数#define n_in 3 隔入向量维数#define n_sample 6 样本数量/bp人工神经网络typedef struct(int lay er num;中间层数量double vn_in50; 中间层权矩阵i,中间层节点最大数量为50double w50n_out;输出层权矩阵double study rate;学习率double accur

2、acy;int maxloop;精度控制参数最大循环次数 bpnet;/sigmoid 函数double fnet(double net)(return l/(l+exp(-net);) 初始化int initbpnet(bpnet *bp);/训练bp网络，样本为x,理想输出为yint tr ainbpn et(bpn et *bp, double xn_sampleint yn_sample |n_out|);使用bp网络int usebpnet(bpnet *bp);主函数int main()训练样本double xn_samplen_in = 08,0.5,0, 09,07。3,1,0

3、.8,0.5,0,0.2,0.3, 02,01,1.3, 0.2,0.7,0.8；理想输出int y n_sample n_out = 0,1,0,1, 0,1, 1,1, 1,0, 1,0)；初始化bp网络bpnet bp;initbpnet(&bp);结构trainbpnet(&bp, x, y); 训练 bp 神经网络usebpnet(&bp);测试 bp 神经网络return 1;)使用bp网络int usebpnet(bpnet *bp)(double inputn_in;double out 1 50;double out2n_out; /outl 为中间层输出qut2为输出层输出

4、持续执行，除非中断程序while (1)printfc请输入3个数：nn);int ij;for (i = 0; i n_in; i+)scanf_s(n%r &inputi);double tmp;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 = fn

5、et(tmp);)printfc 结果： n);for (i = 0; i n_out; i+)printf(h%.3f h,out2i);printf(nnn);)return 1;)训练bp网络，样本为x,理想输出为yinttrainbpn et (bpn et*bp, doublexn_samplen_in, int yn_sample n_out)double f = (*bp).accuracy;精度学控制参数double a = (bp).studyrate;习率int layernum = (*bp).layernum; 中间层节 点数double vn_in50, w50n_o

6、ut; 权矩阵double chgh50, chgon_out;/修改量矩阵double outl50, out2n_out; 中间层和输 出层输出量int maxloop(*bp).maxloop;最大循环次数int n;double tmp;for (i = 0; i n_in; i+)/复制结构体中的权 矩阵for (j = 0; j layernum; j+)viu = (*bp).viu；for (i = 0; i layernum; i+)for (j = 0; j f & n maxloop; n+) (e = 0;for (i= 0; i n_sample; i+)计算中间层输

7、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);)计算输出层输出向量for (k = 0; k n_out; k+)tmp = 0;for (j = 0; j layernum; j+)tmp = tmp + outlj * wjk;out2k = fnet(tmp);)计算输出层的权修改量for (j = 0; j n_out; j+)chgoj = out2j * (1 - out2j) * (yiu - out2u)； 计算输出误差 for

8、(j = 0; j n_out; j+)e = e + (yiu - out2j) * (yiu-out2ud;计算中间层权修改量for (j = 0; j layernum; j+)tmp = 0;for (k = 0; k n_out; k+)tmp = tmp + wjk * chgok;chghj = tmp * outlj * (1 - outiui)； )修改输出层权矩阵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

9、 (k = 0; k layernum; k+) vuk = v皿k + a * xiu * chghk;)if (n % 10 = 0)printf(误差：e);)printf(总共循环次数：%dnf n);printf(调整后的中间层权矩阵：nn);for (i = 0; i n_in; i+)for (j = 0; j layernum; j+) printf(n%f vij);printf(nnn);)printfc调整后的输出层权矩阵：n);for (i = 0; i layernum; i+) for (j = 0; j n_out; j+) printf(n%f wij);pri

10、ntf(nnn);)把结果复制回结构体for (i = 0; i n_in; i+)for (j = 0; j layernum; j+) (*bp).vij = vij;for (i = 0; i layernum; i+)for (j = 0; j layernum);printf(1，请输入学习率:n“);scanf_s(n %lf&(bp).studyrate);/(bp).studyrate为double型数据，所以必须 是ifprintf（请输入精度控制参数：nn）;scanf_s(m %if &(*bp)accuracy);printf请输入最大循环次数：n）;scanf_s(n%dm, &(*bp).maxloop);int ij;srand(unsigned)time(null);for (i = 0; i n_in; i+)for (j = 0;