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Mesh.pm  view on Meta::CPAN

inputs and 50 outputs sound? :-)


=head1 DESCRIPTION

AI::NeuralNet::Mesh is an optimized, accurate neural network Mesh.
It was designed with accruacy and speed in mind. 

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.

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

README  view on Meta::CPAN

Greetings Perlfolk,

** What is this?

AI::NeuralNet::Mesh is an optimized, accurate neural network Mesh.
It was designed with accruacy and speed in mind. 

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?
Contact Josiah Bryan at <jdb@wcoil.com>

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

mesh.htm  view on Meta::CPAN

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