神经网络算法代码

Ⅳ 神经网络bp算法，求源代码改进

0.30953 0.433872 -0.55118 0.254276 0.032833;
-0.15781 0.186609 -0.10978 0.042804 -0.37464 0.571837 -0.18424 0.104407 -0.30953 0.433872 -0.55118 0.254276 0.032833 -0.13925;

Ⅳ 求神经网络算法的一个代码示例（C、C++或java一类的）

在matlab里建立一个.m的M文件，把代码输进去，保存，运行就可以了。 演示程序是在command 里打demo就可以了找到了 . 你邮箱多少，我只有简单的BP神经网络程序。

Ⅵ 求BP神经网络算法的C++源代码

// AnnBP.cpp: implementation of the CAnnBP class.
//
//////////////////////////////////////////////////////////////////////
#include "StdAfx.h"
#include "AnnBP.h"
#include "math.h"

//////////////////////////////////////////////////////////////////////
// Construction/Destruction
//////////////////////////////////////////////////////////////////////

CAnnBP::CAnnBP()
{
eta1=0.3;
momentum1=0.3;

}

CAnnBP::~CAnnBP()
{

}

double CAnnBP::drnd()
{
return ((double) rand() / (double) BIGRND);
}

/*** 返回-1.0到1.0之间的双精度随机数 ***/
double CAnnBP::dpn1()
{
return (double) (rand())/(32767/2)-1;
}

/*** 作用函数，目前是S型函数 ***/
double CAnnBP::squash(double x)
{
return (1.0 / (1.0 + exp(-x)));
}

/*** 申请1维双精度实数数组 ***/
double* CAnnBP::alloc_1d_dbl(int n)
{
double *new1;

new1 = (double *) malloc ((unsigned) (n * sizeof (double)));
if (new1 == NULL) {
AfxMessageBox("ALLOC_1D_DBL: Couldn't allocate array of doubles\n");
return (NULL);
}
return (new1);
}

/*** 申请2维双精度实数数组 ***/
double** CAnnBP::alloc_2d_dbl(int m, int n)
{
int i;
double **new1;

new1 = (double **) malloc ((unsigned) (m * sizeof (double *)));
if (new1 == NULL) {
AfxMessageBox("ALLOC_2D_DBL: Couldn't allocate array of dbl ptrs\n");
return (NULL);
}

for (i = 0; i < m; i++) {
new1[i] = alloc_1d_dbl(n);
}

return (new1);
}

/*** 随机初始化权值 ***/
void CAnnBP::bpnn_randomize_weights(double **w, int m, int n)
{
int i, j;
for (i = 0; i <= m; i++) {
for (j = 0; j <= n; j++) {
w[i][j] = dpn1();
}
}

}

/*** 0初始化权值 ***/
void CAnnBP::bpnn_zero_weights(double **w, int m, int n)
{
int i, j;

for (i = 0; i <= m; i++) {
for (j = 0; j <= n; j++) {
w[i][j] = 0.0;
}
}

}

/*** 设置随机数种子 ***/
void CAnnBP::bpnn_initialize(int seed)
{
CString msg,s;
msg="Random number generator seed:";
s.Format("%d",seed);
AfxMessageBox(msg+s);
srand(seed);
}

/*** 创建BP网络 ***/
BPNN* CAnnBP::bpnn_internal_create(int n_in, int n_hidden, int n_out)
{
BPNN *newnet;

newnet = (BPNN *) malloc (sizeof (BPNN));
if (newnet == NULL) {
printf("BPNN_CREATE: Couldn't allocate neural network\n");
return (NULL);
}

newnet->input_n = n_in;
newnet->hidden_n = n_hidden;
newnet->output_n = n_out;
newnet->input_units = alloc_1d_dbl(n_in + 1);
newnet->hidden_units = alloc_1d_dbl(n_hidden + 1);
newnet->output_units = alloc_1d_dbl(n_out + 1);

newnet->hidden_delta = alloc_1d_dbl(n_hidden + 1);
newnet->output_delta = alloc_1d_dbl(n_out + 1);
newnet->target = alloc_1d_dbl(n_out + 1);

newnet->input_weights = alloc_2d_dbl(n_in + 1, n_hidden + 1);
newnet->hidden_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);

newnet->input_prev_weights = alloc_2d_dbl(n_in + 1, n_hidden + 1);
newnet->hidden_prev_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);

return (newnet);

}

/* 释放BP网络所占地内存空间 */
void CAnnBP::bpnn_free(BPNN *net)
{
int n1, n2, i;

n1 = net->input_n;
n2 = net->hidden_n;

free((char *) net->input_units);
free((char *) net->hidden_units);
free((char *) net->output_units);

free((char *) net->hidden_delta);
free((char *) net->output_delta);
free((char *) net->target);

for (i = 0; i <= n1; i++) {
free((char *) net->input_weights[i]);
free((char *) net->input_prev_weights[i]);
}
free((char *) net->input_weights);
free((char *) net->input_prev_weights);

for (i = 0; i <= n2; i++) {
free((char *) net->hidden_weights[i]);
free((char *) net->hidden_prev_weights[i]);
}
free((char *) net->hidden_weights);
free((char *) net->hidden_prev_weights);

free((char *) net);
}

/*** 创建一个BP网络，并初始化权值***/
BPNN* CAnnBP::bpnn_create(int n_in, int n_hidden, int n_out)
{
BPNN *newnet;

newnet = bpnn_internal_create(n_in, n_hidden, n_out);

#ifdef INITZERO
bpnn_zero_weights(newnet->input_weights, n_in, n_hidden);
#else
bpnn_randomize_weights(newnet->input_weights, n_in, n_hidden);
#endif
bpnn_randomize_weights(newnet->hidden_weights, n_hidden, n_out);
bpnn_zero_weights(newnet->input_prev_weights, n_in, n_hidden);
bpnn_zero_weights(newnet->hidden_prev_weights, n_hidden, n_out);

return (newnet);

}

void CAnnBP::bpnn_layerforward(double *l1, double *l2, double **conn, int n1, int n2)
{
double sum;
int j, k;

/*** 设置阈值 ***/
l1[0] = 1.0;

/*** 对于第二层的每个神经元 ***/
for (j = 1; j <= n2; j++) {

/*** 计算输入的加权总和 ***/
sum = 0.0;
for (k = 0; k <= n1; k++) {
sum += conn[k][j] * l1[k];
}
l2[j] = squash(sum);
}
}

/* 输出误差 */
void CAnnBP::bpnn_output_error(double *delta, double *target, double *output, int nj, double *err)
{
int j;
double o, t, errsum;

errsum = 0.0;
for (j = 1; j <= nj; j++) {
o = output[j];
t = target[j];
delta[j] = o * (1.0 - o) * (t - o);
errsum += ABS(delta[j]);
}
*err = errsum;

}

/* 隐含层误差 */
void CAnnBP::bpnn_hidden_error(double *delta_h, int nh, double *delta_o, int no, double **who, double *hidden, double *err)
{
int j, k;
double h, sum, errsum;

errsum = 0.0;
for (j = 1; j <= nh; j++) {
h = hidden[j];
sum = 0.0;
for (k = 1; k <= no; k++) {
sum += delta_o[k] * who[j][k];
}
delta_h[j] = h * (1.0 - h) * sum;
errsum += ABS(delta_h[j]);
}
*err = errsum;
}

/* 调整权值 */
void CAnnBP::bpnn_adjust_weights(double *delta, int ndelta, double *ly, int nly, double **w, double **oldw, double eta, double momentum)
{
double new_dw;
int k, j;

ly[0] = 1.0;
for (j = 1; j <= ndelta; j++) {
for (k = 0; k <= nly; k++) {
new_dw = ((eta * delta[j] * ly[k]) + (momentum * oldw[k][j]));
w[k][j] += new_dw;
oldw[k][j] = new_dw;
}
}

}

/* 进行前向运算 */
void CAnnBP::bpnn_feedforward(BPNN *net)
{
int in, hid, out;

in = net->input_n;
hid = net->hidden_n;
out = net->output_n;

/*** Feed forward input activations. ***/
bpnn_layerforward(net->input_units, net->hidden_units,
net->input_weights, in, hid);
bpnn_layerforward(net->hidden_units, net->output_units,
net->hidden_weights, hid, out);

}

/* 训练BP网络 */
void CAnnBP::bpnn_train(BPNN *net, double eta, double momentum, double *eo, double *eh)
{
int in, hid, out;
double out_err, hid_err;

in = net->input_n;
hid = net->hidden_n;
out = net->output_n;

/*** 前向输入激活 ***/
bpnn_layerforward(net->input_units, net->hidden_units,
net->input_weights, in, hid);
bpnn_layerforward(net->hidden_units, net->output_units,
net->hidden_weights, hid, out);

/*** 计算隐含层和输出层误差 ***/
bpnn_output_error(net->output_delta, net->target, net->output_units,
out, &out_err);
bpnn_hidden_error(net->hidden_delta, hid, net->output_delta, out,
net->hidden_weights, net->hidden_units, &hid_err);
*eo = out_err;
*eh = hid_err;

/*** 调整输入层和隐含层权值 ***/
bpnn_adjust_weights(net->output_delta, out, net->hidden_units, hid,
net->hidden_weights, net->hidden_prev_weights, eta, momentum);
bpnn_adjust_weights(net->hidden_delta, hid, net->input_units, in,
net->input_weights, net->input_prev_weights, eta, momentum);
}

/* 保存BP网络 */
void CAnnBP::bpnn_save(BPNN *net, char *filename)
{
CFile file;
char *mem;
int n1, n2, n3, i, j, memcnt;
double dvalue, **w;
n1 = net->input_n; n2 = net->hidden_n; n3 = net->output_n;
printf("Saving %dx%dx%d network to '%s'\n", n1, n2, n3, filename);
try
{
file.Open(filename,CFile::modeWrite|CFile::modeCreate|CFile::modeNoTruncate);
}
catch(CFileException* e)
{
e->ReportError();
e->Delete();
}

file.Write(&n1,sizeof(int));
file.Write(&n2,sizeof(int));
file.Write(&n3,sizeof(int));

memcnt = 0;
w = net->input_weights;
mem = (char *) malloc ((unsigned) ((n1+1) * (n2+1) * sizeof(double)));
// mem = (char *) malloc (((n1+1) * (n2+1) * sizeof(double)));
for (i = 0; i <= n1; i++) {
for (j = 0; j <= n2; j++) {
dvalue = w[i][j];
//fast(&mem[memcnt], &dvalue, sizeof(double));
fast(&mem[memcnt], &dvalue, sizeof(double));
memcnt += sizeof(double);

}
}

file.Write(mem,sizeof(double)*(n1+1)*(n2+1));
free(mem);

memcnt = 0;
w = net->hidden_weights;
mem = (char *) malloc ((unsigned) ((n2+1) * (n3+1) * sizeof(double)));
// mem = (char *) malloc (((n2+1) * (n3+1) * sizeof(double)));
for (i = 0; i <= n2; i++) {
for (j = 0; j <= n3; j++) {
dvalue = w[i][j];
fast(&mem[memcnt], &dvalue, sizeof(double));
// fast(&mem[memcnt], &dvalue, sizeof(double));
memcnt += sizeof(double);
}
}

file.Write(mem, (n2+1) * (n3+1) * sizeof(double));
// free(mem);

file.Close();
return;
}

/* 从文件中读取BP网络 */
BPNN* CAnnBP::bpnn_read(char *filename)
{
char *mem;
BPNN *new1;
int n1, n2, n3, i, j, memcnt;
CFile file;

try
{
file.Open(filename,CFile::modeRead|CFile::modeCreate|CFile::modeNoTruncate);
}
catch(CFileException* e)
{
e->ReportError();
e->Delete();
}

// printf("Reading '%s'\n", filename);// fflush(stdout);

file.Read(&n1, sizeof(int));
file.Read(&n2, sizeof(int));
file.Read(&n3, sizeof(int));

new1 = bpnn_internal_create(n1, n2, n3);

// printf("'%s' contains a %dx%dx%d network\n", filename, n1, n2, n3);
// printf("Reading input weights..."); // fflush(stdout);

memcnt = 0;
mem = (char *) malloc (((n1+1) * (n2+1) * sizeof(double)));

file.Read(mem, ((n1+1)*(n2+1))*sizeof(double));
for (i = 0; i <= n1; i++) {
for (j = 0; j <= n2; j++) {
//fast(&(new1->input_weights[i][j]), &mem[memcnt], sizeof(double));
fast(&(new1->input_weights[i][j]), &mem[memcnt], sizeof(double));
memcnt += sizeof(double);
}
}
free(mem);

// printf("Done\nReading hidden weights..."); //fflush(stdout);

memcnt = 0;
mem = (char *) malloc (((n2+1) * (n3+1) * sizeof(double)));

file.Read(mem, (n2+1) * (n3+1) * sizeof(double));
for (i = 0; i <= n2; i++) {

for (j = 0; j <= n3; j++) {
//fast(&(new1->hidden_weights[i][j]), &mem[memcnt], sizeof(double));
fast(&(new1->hidden_weights[i][j]), &mem[memcnt], sizeof(double));
memcnt += sizeof(double);

}
}
free(mem);
file.Close();

printf("Done\n"); //fflush(stdout);

bpnn_zero_weights(new1->input_prev_weights, n1, n2);
bpnn_zero_weights(new1->hidden_prev_weights, n2, n3);

return (new1);
}

void CAnnBP::CreateBP(int n_in, int n_hidden, int n_out)
{
net=bpnn_create(n_in,n_hidden,n_out);
}

void CAnnBP::FreeBP()
{
bpnn_free(net);

}

void CAnnBP::Train(double *input_unit,int input_num, double *target,int target_num, double *eo, double *eh)
{
for(int i=1;i<=input_num;i++)
{
net->input_units[i]=input_unit[i-1];
}

for(int j=1;j<=target_num;j++)
{
net->target[j]=target[j-1];
}
bpnn_train(net,eta1,momentum1,eo,eh);

}

void CAnnBP::Identify(double *input_unit,int input_num,double *target,int target_num)
{
for(int i=1;i<=input_num;i++)
{
net->input_units[i]=input_unit[i-1];
}
bpnn_feedforward(net);
for(int j=1;j<=target_num;j++)
{
target[j-1]=net->output_units[j];
}
}

void CAnnBP::Save(char *filename)
{
bpnn_save(net,filename);

}

void CAnnBP::Read(char *filename)
{
net=bpnn_read(filename);
}

void CAnnBP::SetBParm(double eta, double momentum)
{
eta1=eta;
momentum1=momentum;

}

void CAnnBP::Initialize(int seed)
{
bpnn_initialize(seed);

}

Ⅶ 求神经网络算法完整程序

%创建一个新的前向神经网络
net=newff(minmax(P),[17,2],{'tansig','logsig'},'traingdm')
%当前输入层权值和阀值
inputWeight=net.IW{1,1}
inputbias=net.b{1}
% 当前网络层权值和阀值
layerWeight=net.LW{2,1}
layerbias=net.b{2}
% 设置训练参数
net.trainParam.show=100;
net.trainParam.lr=0.15;
net.trainParam.mc=0.9;
net.trainParam.epochs=2000;
net.trainParam.goal=1e-3;
net.trainParam.time=inf;
net.trainParam.min_grad=1e-10;
net=train(net,P,T);
你可以根据这个改改就行，程序前面加上你的输入向量P跟目标向量T，别忘了对P进行归一化。好运！

Ⅷ 神经网络算法原理

4.2.1 概述

人工神经网络的研究与计算机的研究几乎是同步发展的。1943年心理学家McCulloch和数学家Pitts合作提出了形式神经元的数学模型，20世纪50年代末，Rosenblatt提出了感知器模型，1982年，Hopfiled引入了能量函数的概念提出了神经网络的一种数学模型，1986年，Rumelhart及LeCun等学者提出了多层感知器的反向传播算法等。

神经网络技术在众多研究者的努力下，理论上日趋完善，算法种类不断增加。目前，有关神经网络的理论研究成果很多，出版了不少有关基础理论的着作，并且现在仍是全球非线性科学研究的热点之一。

神经网络是一种通过模拟人的大脑神经结构去实现人脑智能活动功能的信息处理系统，它具有人脑的基本功能，但又不是人脑的真实写照。它是人脑的一种抽象、简化和模拟模型，故称之为人工神经网络（边肇祺，2000）。

人工神经元是神经网络的节点，是神经网络的最重要组成部分之一。目前，有关神经元的模型种类繁多，最常用最简单的模型是由阈值函数、Sigmoid 函数构成的模型（图 4-3）。

储层特征研究与预测

以上算法是对每个样本作权值修正，也可以对各个样本计算δ_j后求和，按总误差修正权值。

Ⅸ 急求BP神经网络算法，用java实现！！！

见附件，一个基本的用java编写的BP网络代码。

BP（Back Propagation）神经网络是86年由Rumelhart和McCelland为首的科学家小组提出，是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hidden layer)和输出层(output layer)。