ANNOUNCE: AI::NeuralNet::Mesh v0.31


Subject: ANNOUNCE: AI::NeuralNet::Mesh v0.31
From: Josiah Bryan (jdb@wcoil.com)
Date: Fri Aug 25 2000 - 12:16:02 MET DST


Greetings!

Announcing the release of:

    AI::NeuralNet::Mesh, version 0.31

(AI::NeuralNet::Mesh is an optimized, accurate neural network Mesh
written in Perl.)

Download at: (***recomended download url***):
http://josiah.countystart.com/modules/get.pl?mesh:ai.fuzzy

This module has also been released on the CPAN as:
   file: $CPAN/authors/id/J/JB/JBRYAN/AI-NeuralNet-Mesh-0.31.zip
  size: 82087 bytes

** What's new?

>From the POD:
This is version *0.31*, the second release of this module.

In this version, I have included three major features. Also in this
release I have included two minor fixes which increase the learning
speed of networks. I also fixed a bug in the load_pcx() method which
prevented it from loading the PCX::Loader module correctly. This
version also has the ability to have negative weights in the network.

The major features added are:

LAYER SIZES

Rodin Porrata once suggested it would be good to have control over
each layer's node size. Well, Rodin, here you go. Each layer can
have a custom number of nodes, which you can set in two ways, detailed
in the new() constructor, below. Layer sizes are preserved across load()
and save() calls.

LAYER EXTENSION

With the ability to have custom layer sizes, I have also included the
ability to extend layer sizes after network construction. You can add
nodes with extend() or extend_layer() after the network is constructed
or loaded.

CUSTOM NODE ACTIVATION

Ahh, and another treat. You can choose from one of four activation
functions and set the activation function by layer, or you can even
set each individual node to a seperate activation function. Possible
activation types are:
        'linear' (simply transfer sum of inputs as output)
        'sigmoid' (also called 'sigmoid_1') (0 or 1, threshold based)
        'sigmoid_2' (-1,0,1, threshold based)
        user specified (passed as a CODE ref.)

You can also customize threshold levels on a per-layer, or per-node basis.

** What is this?

AI::NeuralNet::Mesh is an optimized, accurate neural network Mesh
written in Perl. It was designed with accruacy and speed in mind.
This better learning accuracy (by twenty-three percent), as well
as a much faster learning rate and run speed over any other neural
network simulater that I know of in Perl.(*)

Included are fifteen seperate example scripts. In particular demonstration
of the accuracy and speed of this network model, check out ex_add2.pl,
ex_dow.pl, and ex_add.pl.

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

** 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>

(*) In regards to the speed claim: I only know of two neural networks
in Perl, AI::NeuralNet::Mesh (this module) and AI::NeuralNet::BackProp,
both of which I wrote. If someone else has written a neural-net in Perl
I would love to see how or what techniques you have used. I am sorry if
I have missed anybody's Perl neural-net. If I have, it was not an
intentional error on my part.

--
Josiah Bryan
VP of Product Development
TDCJ, Inc.
"Anything is possible."

vp@tdcj.com http://www.josiah.countystart.com/

Tel: 937.316.6256

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