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<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-&gt;learn([0,0],[0]);
        $net-&gt;learn([0,1],[0]);
        $net-&gt;learn([1,0],[0]);
        $net-&gt;learn([1,1],[1]);

        # Present it with two test cases
        my $result_bit_1 = $net-&gt;run([0,1])-&gt;[0];
        my $result_bit_2 = $net-&gt;run([1,1])-&gt;[0];

        # Display the results
        print &quot;AND test with inputs (0,1): $result_bit_1\n&quot;;
        print &quot;AND test with inputs (1,1): $result_bit_2\n&quot;;

</PRE>
<P>
<HR>
<H1><A NAME="version & updates">VERSION &amp; 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
per layer basis (you can even include your own anonymous subs as 
activation types). You can add sigmoid transfer functions and control
the threshold. You can learn data sets in batch, and load CSV data
set files. You can do almost anything you need to with this module.
This code is deigned to be flexable. Any new ideas for this module?
See AUTHOR, below, for contact info.</P>
<P>This module is designed to also be a customizable, extensable 
neural network simulation toolkit. Through a combination of setting
the $Connection variable and using custom activation functions, as
well as basic package inheritance, you can simulate many different
types of neural network structures with very little new code written
by you.</P>
<P>In this module I have included a more accurate form of ``learning'' for the
mesh. This form preforms descent toward a local error minimum (0) on a 
directional delta, rather than the desired value for that node. This allows
for better, and more accurate results with larger datasets. This module also
uses a simpler recursion technique which, suprisingly, is more accurate than
the original technique that I've used in other ANNs.</P>
<P>
<HR>
<H1><A NAME="exports">EXPORTS</A></H1>
<P>This module exports three functions by default:</P>
<PRE>
        range
        intr
        pdiff
</PRE>
<P>See range() intr() and pdiff() for description of their respective functions.</P>
<P>Also provided are several export tag sets for usage in the form of:</P>
<PRE>
        use AI::NeuralNet::Mesh ':tag';
</PRE>
<P>Tag sets are:</P>
<PRE>
        :default 
            - These functions are always exported.
                - Exports:
                range()
                intr()
                pdiff()

        :all
                - Exports:
                p()
                high()
                low()
                range()
                ramp()
                and_gate()
                or_gate()

        :p
                - Exports:
                p()
                high()
                low()

        :acts
                - Exports:
                ramp()