Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!uunet!mcsun!ukc!strath-cs!cs.glasgow.ac.uk!bru-cc!ma86kbl
From: ma86kbl@cc.brunel.ac.uk (Kam B Lo)
Newsgroups: comp.ai.neural-nets,brunel.neural
Subject: Spectrascopic data
Message-ID: <1456@Terra.cc.brunel.ac.uk>
Date: 10 Apr 90 15:36:32 GMT
Reply-To: ma86kbl@cc.brunel.ac.uk (Kam B Lo)
Organization: Brunel University, Uxbridge, UK
Lines: 14

I am trying to train a three-layered neural network (1 layer of input,
1 hidden layer and 1 output layer) to classify two groups of chemical
compounds by their NIR spectrascopic data using the back-propagation
technique. Each of the NIR spectrum consists of 700 values in the range
of -0.03 to 0.03.

My problem is what preprocessing do I need to do to this data to get it
in an acceptable form for entering into the neural network. I have tried
setting up a network with 700 input cells and assigning them to the 
corresponding values on the spectrum (is this sensible?), but this
always got stuck in a local minimum.

Any help with this problem will be greatly appreciated,
         Kam.