Single-layer perceptron neural network __ Neural network

/***********************************************************************/  /* File: Mc_neuron.h                                                  * *  2014-06-04  //////* Description: Single-layer perceptron neural network header file                                      */
/************************************************ /  

#ifndef _afx_mc_neuron_include_h_
#define _AFX_MC_NEURON_INCLUDE_H_
 
Class Neuron
{public
	
:
	Neuron ();

Public:
	bool  Train (float expect, float mX1, float mX2);
	Float Compute (float mX1, float mX2);
	void  getweight (float &mx1, float &mx2);
	
Private:	
	float activatefunc (float fsum);

Private:
	float m_weight[2];  Weight vector
	float M_bias;       Deviation
	float M_alpha;      Learning Rate
 
};

/***********************************************************************//* File: Mc_neuron.cpp                                                         * * Copyright: Reprint please specify from mni2005 blog [http://blog.csdn.net/mni2005]///* Author: mo * * * Date: 2014-06-04/* Description: Single-layer Perceptron God   

Through the network implementation * * */***********************************************************************/
	#include "Mc_neuron.h" Neuron::neuron () {m_weight[0] = 0.01f;	

	M_WEIGHT[1] = 0.01f;
	M_bias = 0.01f;
M_alpha = 0.01f;  
	}//Activation function float Neuron::activatefunc (float Yn) {if (Yn >=0.0f) {return 1.0f;
	}else{return 0.0f;

	} float Neuron::compute (float mX0, float mX1) {float Yn = m_bias*1.0f + m_weight[0]*mx0 + m_weight[1]* mX1;	
Return Activatefunc (Yn);
	} void Neuron::getweight (float &mx1, float &mx2) {mX1 = m_weight[0];
mX2 = m_weight[1]; BOOL Neuron::train (float Expect, float mX0, float mX1) {Float Yn = Compute (mX0, mX1);
		Adjust weights perceptron vector if (Yn!= expect) {m_weight[0] + = (M_alpha * (expect-yn) * mX0); 
		M_WEIGHT[1] + + (M_alpha * (expect-yn) * mX1); 

		M_bias + + (M_alpha * (expect-yn) * 1.0f);
	return false;
return true;
 }

/***********************************************************************/ /*  File:neurontest.cpp                                                  */*  Copyright: Reprint Please specify the blog from mni2005 [http://blog.csdn.net/ mni2005]    * */*  author: mo Q                                                             */*  Date: 2014-06-04                                                     //*  Description: Complete single layer perceptron neural network test with logic and/or operation                     */***************************** /    #include <stdio.h> #include <stdlib.h> # 		
		Include "Mc_neuron.h" int main (int argc, char* argv[]) {//Logic and Training sample float slps[][3] = {1.0, 1.0, 1.0}, 
{0.0, 1.0, 0.0}, {1.0, 0.0, 0.0}, {0.0, 0.0, 0.0},}; 		
	*///logic or training samples float slps[][3] = {1.0, 1.0, 1.0}, {0.0, 1.0, 1.0}, {1.0, 0.0, 1.0}, {0.0, 0.0, 0.0}, 

	};
	Neuron Neuron;

	BOOL BOK;
		Start training do{BOK = true;		
		   for (int i=0; i<4; i++) {float* PSLP = slps[i]; if (!neuron.
		   Train (Pslp[2], pslp[0], pslp[1]) {BOK = false;

	}}while (!bok); Training over, losingThe weight value float weight1, weight2; Neuron.
	Getweight (weight1, weight2);

	printf ("Weight1=%f, weight2=%f\n", weight1, weight2);		
		Validation Perceptron training result for (int i=0; i<4; i++) {float* PSLP = slps[i];
		float Fvalue = Neuron.compute (Pslp[0], pslp[1]);
	printf ("Value=%f, expect=%f\n", Fvalue, pslp[2]);

	System ("pause");
return 0;
  }