AI-NeuralNet-Mesh
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<B>AI::NeuralNet::Mesh</B> - An optimized, accurate neural network Mesh.
</PRE>
<A NAME="__index__"></A>
<!-- INDEX BEGIN -->
<UL>
<LI><A HREF="#name">NAME</A></LI>
<LI><A HREF="#synopsis">SYNOPSIS</A></LI>
<LI><A HREF="#version & updates">VERSION & UPDATES</A></LI>
<LI><A HREF="#description">DESCRIPTION</A></LI>
<LI><A HREF="#exports">EXPORTS</A></LI>
<LI><A HREF="#methods">METHODS</A></LI>
<LI><A HREF="#custom activation functions">CUSTOM ACTIVATION FUNCTIONS</A></LI>
<LI><A HREF="#variables">VARIABLES</A></LI>
<LI><A HREF="#custom network connectors">CUSTOM NETWORK CONNECTORS</A></LI>
<LI><A HREF="#what can it do">WHAT CAN IT DO?</A></LI>
<LI><A HREF="#examples">EXAMPLES</A></LI>
<LI><A HREF="#other included packages">OTHER INCLUDED PACKAGES</A></LI>
<LI><A HREF="#bugs">BUGS</A></LI>
<LI><A HREF="#author">AUTHOR</A></LI>
<LI><A HREF="#thanks">THANKS</A></LI>
<LI><A HREF="#download">DOWNLOAD</A></LI>
<LI><A HREF="#mailing list">MAILING LIST</A></LI>
</UL>
<!-- INDEX END -->
<HR>
<P>
<H1><A NAME="name">NAME</A></H1>
<P>AI::NeuralNet::Mesh - An optimized, accurate neural network Mesh.</P>
<P>
<HR>
<H1><A NAME="synopsis">SYNOPSIS</A></H1>
<PRE>
use AI::NeuralNet::Mesh;
# Create a mesh with 2 layers, 2 nodes/layer, and one output node.
my $net = new AI::NeuralNet::Mesh(2,2,1);
# Teach the network the AND function
$net->learn([0,0],[0]);
$net->learn([0,1],[0]);
$net->learn([1,0],[0]);
$net->learn([1,1],[1]);
# Present it with two test cases
my $result_bit_1 = $net->run([0,1])->[0];
my $result_bit_2 = $net->run([1,1])->[0];
# Display the results
print "AND test with inputs (0,1): $result_bit_1\n";
print "AND test with inputs (1,1): $result_bit_2\n";
</PRE>
<P>
<HR>
<H1><A NAME="version & updates">VERSION & UPDATES</A></H1>
<P>This is version <STRONG>0.43</STRONG>, the second release of this module.</P>
<P>With this version I have gone through and tuned up many area
of this module, including the descent algorithim in learn(),
as well as four custom activation functions, and several export
tag sets. With this release, I have also included a few
new and more practical example scripts. (See ex_wine.pl) This release
also includes a simple example of an ALN (Adaptive Logic Network) made
with this module. See ex_aln.pl. Also in this release is support for
loading data sets from simple CSV-like files. See the <A HREF="#item_load_set"><CODE>load_set()</CODE></A> method
for details. This version also fixes a big bug that I never knew about
until writing some demos for this version - that is, when trying to use
more than one output node, the mesh would freeze in learning. But, that
is fixed now, and you can have as many outputs as you want (how does 3
inputs and 50 outputs sound? :-)</P>
<P>
<HR>
<H1><A NAME="description">DESCRIPTION</A></H1>
<P>AI::NeuralNet::Mesh is an optimized, accurate neural network Mesh.
It was designed with accruacy and speed in mind.</P>
<P>This network model is very flexable. It will allow for clasic binary
operation or any range of integer or floating-point inputs you care
to provide. With this you can change activation types on a per node or
just very good answers, are also best done with neural networks. A quick,
very good response is often more desirable than a more accurate answer which
takes longer to compute. This is especially true in robotics or industrial
controller applications. Predictions of behavior and general analysis of
data are also affairs for neural networks. In the financial arena, consumer
loan analysis and financial forecasting make good applications. New network
designers are working on weather forecasts by neural networks (Myself
included). Currently, doctors are developing medical neural networks as an
aid in diagnosis. Attorneys and insurance companies are also working on
neural networks to help estimate the value of claims.</P>
<P>Neural networks are poor at precise calculations and serial processing. They
are also unable to predict or recognize anything that does not inherently
contain some sort of pattern. For example, they cannot predict the lottery,
since this is a random process. It is unlikely that a neural network could
be built which has the capacity to think as well as a person does for two
reasons. Neural networks are terrible at deduction, or logical thinking and
the human brain is just too complex to completely simulate. Also, some
problems are too difficult for present technology. Real vision, for
example, is a long way off.</P>
<P>In short, Neural Networks are poor at precise calculations, but good at
association, evaluation, and pattern recognition.</P>
<P>
<HR>
<H1><A NAME="examples">EXAMPLES</A></H1>
<P>Included are several example files in the ``examples'' directory from the
distribution ZIP file. Each of the examples includes a short explanation
at the top of the file. Each of these are ment to demonstrate simple, yet
practical (for the most part :-) uses of this module.</P>
<P>
<HR>
<H1><A NAME="other included packages">OTHER INCLUDED PACKAGES</A></H1>
<P>These packages are not designed to be called directly, they are for internal use. They are
listed here simply for your refrence.</P>
<DL>
<DT><STRONG><A NAME="item_AI%3A%3ANeuralNet%3A%3AMesh%3A%3Anode">AI::NeuralNet::Mesh::node</A></STRONG><BR>
<DD>
This is the worker package of the mesh. It implements all the individual nodes of the mesh.
It might be good to look at the source for this package (in the Mesh.pm file) if you
plan to do a lot of or extensive custom node activation types.
<P></P>
<DT><STRONG><A NAME="item_AI%3A%3ANeuralNet%3A%3AMesh%3A%3Acap">AI::NeuralNet::Mesh::cap</A></STRONG><BR>
<DD>
This is applied to the input layer of the mesh to prevent the mesh from trying to recursivly
adjust weights out throug the inputs.
<P></P>
<DT><STRONG><A NAME="item_AI%3A%3ANeuralNet%3A%3AMesh%3A%3Aoutput">AI::NeuralNet::Mesh::output</A></STRONG><BR>
<DD>
This is simply a data collector package clamped onto the output layer to record the data
as it comes out of the mesh.
<P></P></DL>
<P>
<HR>
<H1><A NAME="bugs">BUGS</A></H1>
<P>This is a beta release of <CODE>AI::NeuralNet::Mesh</CODE>, and that holding true, I am sure
there are probably bugs in here which I just have not found yet. If you find bugs in this module, I would
appreciate it greatly if you could report them to me at <EM><<A HREF="mailto:jdb@wcoil.com">jdb@wcoil.com</A>></EM>,
or, even better, try to patch them yourself and figure out why the bug is being buggy, and
send me the patched code, again at <EM><<A HREF="mailto:jdb@wcoil.com">jdb@wcoil.com</A>></EM>.</P>
<P>
<HR>
<H1><A NAME="author">AUTHOR</A></H1>
<P>Josiah Bryan <EM><<A HREF="mailto:jdb@wcoil.com">jdb@wcoil.com</A>></EM></P>
<P>Copyright (c) 2000 Josiah Bryan. All rights reserved. This program is free software;
you can redistribute it and/or modify it under the same terms as Perl itself.</P>
<P>The <CODE>AI::NeuralNet::Mesh</CODE> and related modules are free software. THEY COME WITHOUT WARRANTY OF ANY KIND.</P>
<P>$Id: AI::NeuralNet::Mesh.pm, v0.43 2000/15/09 03:29:08 josiah Exp $</P>
<P>
<HR>
<H1><A NAME="thanks">THANKS</A></H1>
<P>Below are a list of the people that have contributed in some way to this module (no particular order):</P>
<PRE>
Rodin Porrata, rodin@ursa.llnl.gov
Randal L. Schwartz, merlyn@stonehedge.com
Michiel de Roo, michiel@geo.uu.nl
</PRE>
<PRE>
Thanks to Randal and Michiel for spoting some documentation and makefile bugs in the last release.
Thanks to Rodin for continual suggetions and questions about the module and more.</PRE>
<P>
<HR>
<H1><A NAME="download">DOWNLOAD</A></H1>
<P>You can always download the latest copy of AI::NeuralNet::Mesh
from <A HREF="http://www.josiah.countystart.com/modules/get.pl?mesh:pod">http://www.josiah.countystart.com/modules/get.pl?mesh:pod</A></P>
<P>
<HR>
<H1><A NAME="mailing list">MAILING LIST</A></H1>
<P>A mailing list has been setup for AI::NeuralNet::Mesh and AI::NeuralNet::BackProp.
The list is for discussion of AI and neural net related topics as they pertain to
AI::NeuralNet::BackProp and AI::NeuralNet::mesh. I will also announce in the group
each time a new release of AI::NeuralNet::Mesh is available.</P>
The list address is: <A HREF="mailto:ai-neuralnet-backprop@egroups.com">ai-neuralnet-backprop@egroups.com</A> <BR>
To subscribe, send a blank email to: <A HREF="mailto:ai-neuralnet-backprop-subscribe@egroups.com">ai-neuralnet-backprop-subscribe@egroups.com</A>
<BR><BR><BR>
<HR>
<A HREF="http://www.josiah.countystart.com/modules/get.pl?mesh:(c)"><B>AI::NeuralNet::Mesh</B></A> - An optimized, accurate neural network Mesh. By <A HREF="mailto:jdb@wcoil.com"><B>Josiah Bryan</B></A>.
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