This is version 0.77, a complete internal upgrade from version 0.42. A new feature is the introduction of a randomness factor in the network, optional to disable. The restriction on 0s are removed, so you can run any network you like. Included is an improved learn() function, and a much more accurate internal fixed-point system for learning. Also included is automated learning of input sets. See learn_set() and learn_rand_set(). Be sure to look for the two brand new examples, finance.pl and images.pl. finance.pl demonstrates simple DOW prediction based on 6 months of data in 1989, and images.pl demonstrates simple bitmap classification. Many other examples were updated and modfied. 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. 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