NOTE: This is version 0.42, the patch for the first public release 
which was version 0.40. It includes several updates and fixes, 
including more 'intelligent' learning, smaller memory size, actual 
loading and saving of network, internal support for strings as network
maps and targets, and integrated support for direct loading of PCX-format 
bitmap files, as well as 11 new examples.

AI::NeuralNet::BackProp is a simply back-propagation,
feed-foward neural network designed to learn using
a generalization of the Delta rule and a bit of Hopefield
theory. Still in beta stages.

Be sure to checkout the ./examples/ directory for 17 different
example scripts using the AI::NeuralNet::BackProp network.

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. It uses the -IDEA- of back-propagation
for error correction, with the -IDEA- of the delta
rule and hopefield theory, as I understand them. So, 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! :-)
	
Thankyou all for your help.

~ Josiah
	
jdb@wcoil.com
http://www.josiah.countystart.com/modules/AI/cgi-bin/rec.pl - dowload latest dist