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From: mcguire@fornax.UUCP (Michael McGuire)
Newsgroups: comp.ai.neural-nets
Subject: Normalization of Neural Net Inputs
Keywords: BP, Normalization
Message-ID: <2654@fornax.UUCP>
Date: 7 May 91 18:31:46 GMT
Organization: School of Computing Science, SFU, Burnaby, B.C. Canada
Lines: 17

A couple of weeks back I posted a question regarding the effects of
scaling inputs into a back-prop net, and received several replies
confirming that it should have no affect on classification performance.

This leads to a question on normalizing network inputs. Scaling usually
refers to biasing the entire input pattern set by some fixed amount. 
What is the effect of normalizing each input pattern individually based
on some criterion in attempts to remove pattern variation caused by
such things as a signals dynamic range? Does anyone have any experience
with different normalization techniques?
Thanks in advance.

Mike McGuire
Engineering Science
Simon Fraser University
Canada
e-mail:mcguire@cs.sfu.ca