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 *
 *  It's been so heavily modified that it bears little resemblance to the
 *  original, but credit should be given where credit is due.  Therefore ...
 *
 *  Copyright (c) 2003 Charles River Media.  All rights reserved.
 * 
 *  Redistribution and use in source and binary forms, with or without
 *  modification, is hereby granted without fee provided that the following
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 * 
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 *    notice, this list of conditions and the following disclaimer.  2.
 *    Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
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 *    Neither the name of Charles River Media nor the names of its
 *    contributors may be used to endorse or promote products derived from
 *    this software without specific prior written permission.
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 * THIS SOFTWARE IS PROVIDED BY CHARLES RIVER MEDIA AND CONTRIBUTORS 'AS IS'
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lib/AI/NeuralNet/Simple.pm  view on Meta::CPAN

=head1 SEE ALSO

L<AI::FANN> - Perl wrapper for the Fast Artificial Neural Network library

L<AI::NNFlex> - A base class for implementing neural networks 

L<AI::NeuralNet::BackProp> - A simple back-prop neural net that uses Delta's
and Hebbs' rule

"AI Application Programming by M. Tim Jones, copyright (c) by Charles River
Media, Inc.  

The C code in this module is based heavily upon Mr. Jones backpropogation
network in the book.  The "game ai" example in the examples directory is based
upon an example he has graciously allowed me to use.  I I<had> to use it
because it's more fun than many of the dry examples out there :)

"Naturally Intelligent Systems", by Maureen Caudill and Charles Butler,
copyright (c) 1990 by Massachussetts Institute of Technology.

This book is a decent introduction to neural networks in general.  The forward
feed back error propogation is but one of many types.

=head1 AUTHORS

Curtis "Ovid" Poe, C<ovid [at] cpan [dot] org>

Multiple network support, persistence, export of MSE (mean squared error),
training until MSE below a given threshold and customization of the