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
well as basic package inheritance, you can simulate many different
types of neural network structures with very little new code written
by you. (See ex_aln.pl)

As always, included is a cleaned, CSS-ed, HTML-format of the POD docs.

** What's new?

>From the POD:
This is version 0.44, an bug fix for the third release of the module.

This fixed a compatibilty issue that 0.43 had with Perl 5.3.3

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 load_set() 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? :-) Also in this release is output range
limiting via the range() activation function.

** What do you think?

Now I know you people are out there that are using the module...
I can hear the fists hitting the keyboards in frustration. :-) Relieve
some of that frustration by e-mailing me and letting me know what
you think of the module and any suggestions you got. 

Use it, let me know what you all think. This is just a
groud-up write of a neural network, no code stolen or
anything else. Don't expect a classicist view of nerual 
networking here. I simply wrote from operating theory, 
not math theory. Any die-hard neural networking gurus out 
there? Let me know how far off I am with
this code! :-)
	
Regards,

        ~ Josiah Bryan, <jdb@wcoil.com>

Latest Version:

        http://www.josiah.countystart.com/modules/get.pl?mesh:README