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Backpropagation Artificial Neural Network in C++

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20 May 2008GPL38 min read 210.4K   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.
========================================================================
    CONSOLE APPLICATION : ann1Dn Project Overview
========================================================================

AppWizard has created this ann1Dn application for you.  
This file contains a summary of what you will find in each of the files that
make up your ann1Dn application.


ann1Dn.vcproj
    This is the main project file for VC++ projects generated using an Application Wizard. 
    It contains information about the version of Visual C++ that generated the file, and 
    information about the platforms, configurations, and project features selected with the
    Application Wizard.

ann1Dn.cpp
    This is the main application source file.

/////////////////////////////////////////////////////////////////////////////
Other standard files:

StdAfx.h, StdAfx.cpp
    These files are used to build a precompiled header (PCH) file
    named ann1Dn.pch and a precompiled types file named StdAfx.obj.

/////////////////////////////////////////////////////////////////////////////
Other notes:

AppWizard uses "TODO:" comments to indicate parts of the source code you
should add to or customize.

/////////////////////////////////////////////////////////////////////////////

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License

This article, along with any associated source code and files, is licensed under The GNU General Public License (GPLv3)


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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