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Backpropagation Artificial Neural Network in C++

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20 May 2008GPL38 min read 207.1K   10.2K   104  
This article demonstrates a backpropagation artificial neural network console application with validation and test sets for performance estimation using uneven distribution metrics.


#include "stdAfx.h"
#include "neuron.h"



/*
                                                    ANN neuron link
                                                                                                                              */
//////////////////////////////////////////////////constructor/destructor////////////////////////////////////////////////////////
ANLink::ANLink(ANeuron *pinn, ANeuron *poutn, float in, float w, float add) : dwprv(0.0f)
{
        pinput_neuron = pinn;           //this neuron
        poutput_neuron  = poutn;        //out neuron

        ival = in;	                //input val
        this->w = w;                    //weight

        iadd = add;
}
ANLink::~ANLink()
{
}
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////








/*
                                                     ANN neuron
                                                                                                                              */
//////////////////////////////////////////////////constructor/destructor////////////////////////////////////////////////////////
ANeuron::ANeuron(): oval(0), delta(0), function(LINEAR)
{
}
ANeuron::~ANeuron()
{
        for (int i = 0; i < get_input_links_number(); i++)   //delete input links
                delete inputs[i];
}
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////


///////////////////////////////////add input link//////////////////////////////////////////////////
void ANeuron::add_bias()
{        
        inputs.push_back(new ANLink(this));
}
void ANeuron::add_input(ANeuron *poutn)       //add input link
{
        //poutn - N from previous layer
        ANLink *plnk = new ANLink(this, poutn); 
        inputs.push_back(plnk);
        if (poutn)                 
                poutn->outputs.push_back(plnk);        
}
///////////////////////////////////////////////////////////////////////////////////////////////////

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Written By
Engineer
Russian Federation Russian Federation
Highly skilled Engineer with 14 years of experience in academia, R&D and commercial product development supporting full software life-cycle from idea to implementation and further support. During my academic career I was able to succeed in MIT Computers in Cardiology 2006 international challenge, as a R&D and SW engineer gain CodeProject MVP, find algorithmic solutions to quickly resolve tough customer problems to pass product requirements in tight deadlines. My key areas of expertise involve Object-Oriented
Analysis and Design OOAD, OOP, machine learning, natural language processing, face recognition, computer vision and image processing, wavelet analysis, digital signal processing in cardiology.

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