Greetings Perlfolk, ** What is this? 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. ** What's new? From the POD: This is version 0.89. In this version I have included a new feature, output range limits, as well as automatic crunching of run() and learn*() inputs. Included in the examples directory are seven new practical-use example scripts. Also implemented in this version is a much cleaner learning function for individual neurons which is more accurate than previous verions and is based on the LMS rule. See range() for information on output range limits. I have also updated the load() and save() methods so that they do not depend on Storable anymore. In this version you also have the choice between three network topologies, two not as stable, and the third is the default which has been in use for the previous four versions. Checkout the nifty HTML-format docs in "docs.htm" ** 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. Especially you guys in HP Labs and at Xerox! :-) 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! :-) Regards, ~ Josiah Bryan, Latest Version: http://www.josiah.countystart.com/modules/AI/cgi-bin/rec.pl?README