bpnet.cpp

此程序实现BP网络对异或地模拟

// BPnet.cpp: implementation of the BPnet class.
//
//////////////////////////////////////////////////////////////////////

#include "stdafx.h"
#include "BPtoXorS.h"
#include "BPnet.h"

#ifdef _DEBUG
#undef THIS_FILE
static char THIS_FILE[]=__FILE__;
#define new DEBUG_NEW
#endif

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

BPnet::BPnet()
{
	//input[]={0.05 , 0.95}; //为什么这样给数组赋值在vc++中就会出错 ?
	input[0] = 0.05;
	input[1] = 0.95; 
	output = 1.0;
	//threshold_m[] = {rand() , rand()};
	/*double AverageRandom(double min,double max)
        {
        int minInteger = (int)(min*10000);
        int maxInteger = (int)(max*10000);
        int randInteger = rand()*rand();
        int diffInteger = maxInteger - minInteger;
        int resultInteger = randInteger % diffInteger + minInteger;
        return resultInteger/10000.0;
        }*/
	srand( (unsigned)time( NULL ) );

	for(int i=0; i<2; i++)
		threshold_m[i] = AverageRandom(-1,1);
	threshold_o = AverageRandom(-1,1);
	//w_middle[] = {rand() , rand() , rand() , rand()};
	for(i=0; i<4; i++)
		w_middle[i] = AverageRandom(-1,1);
	//w_output[] = {rand() , rand()};
	for(i=0; i<2; i++)
		w_output[i] = AverageRandom(-1,1);
	for(i=0; i<2; i++)
	{
		middle_s[i] = 0;		//zero
		middle_o[i] = 0;		//zero
	}
	output_s = 0;
	output_o = 0;
	
	u0 = 0.3;
	a = 0.5;
	b = 0.5;
	error = 5.0;
}

BPnet::~BPnet()
{

}

void BPnet::train()
{
	//计算中间层激活值
 	for(int i=0; i<2; i++)
		middle_s[0] += w_middle[i] * input[i];
	middle_s[0] -= threshold_m[0];
	
	for(i=0; i<2; i++)
		middle_s[1] += w_middle[i+2] * input[i];
	middle_s[1] -= threshold_m[1];

	//中间层的输出值
	for(i=0; i<2; i++)
		middle_o[i] = 0.5 * (1 + tan(middle_s[i] / u0));

	//输出层的激活值
	for(i=0; i<2; i++)
		output_s +=w_output[i] * middle_o[i];
	output_s -= threshold_o;

	//输出层的输出值
	output_o = 0.5 * (1 + tan(output_s / u0));



	//输出误差的逆传播
	//输出层的矫正误差
	error_o = (output - output_o) * output_o * (1 + output_o);

	//中间层个单元的矫正误差
	for(i=0; i<2; i++)
		error_m[i] = w_output[i] * error_o * middle_o[i] *(1 + middle_o[i]);
	
	//重新计算输出层到中间层的权值和输出层的阈值
	for(i=0; i<2; i++)
		w_output[i] += a * error_o * middle_o[i];
	threshold_o += a * error_o;
	
	//重新计算中间层到输入层的权值和中间层的阈值
    w_middle[0] += b * error_m[0] * input[0]; 
	w_middle[1] += b * error_m[1] * input[0]; 
	w_middle[2] += b * error_m[0] * input[1]; 
	w_middle[3] += b * error_m[1] * input[1]; 
	threshold_m[0] += b * error_m[0];
	threshold_m[1] += b * error_m[1];

	//计算误差
	error = fabs(output - output_o);
	


	


}
//求随机数
double BPnet::AverageRandom(double min, double max)
{

        int minInteger = (int)(min*10000);
        int maxInteger = (int)(max*10000);
        int randInteger = rand()*rand();
        int diffInteger = maxInteger - minInteger;
        int resultInteger = randInteger % diffInteger + minInteger;
        return resultInteger/10000.0;
       
}