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From: mcdonald@undeed.uucp (Bruce J McDonald)
Newsgroups: comp.ai.neural-nets
Subject: Back-propagation
Message-ID: <1990Nov06.152539.17957@undeed.uucp>
Date: 6 Nov 90 15:25:39 GMT
Organization: Univ. Natal, Durban, S. Africa
Lines: 35


Hello

I am designing a reconfigurable neural-net chip which is arranged
as several layers, each containing a number of processing elements
( nodes ).  The NN is designed for recall operation only as the 
necessary logic to support comprehensive training would result in
each node becoming too large and would also mean that each node
would need a state machine to control it.  I have instead decided
that each input weight for each node in the NN can be set externally
using a single serial data channel which is multiplexed by an overall
controller to each weight in each node.  To keep things simple the 
width of input and output to each node is one-bit wide and this
technique allows for a very compact node design.  All training and
weight adjustment is done off-chip.

The key to this approach is a generic ( remaining within the limits
of digital data representation - to some, a crippling limitation) 
NN simulator and trainer.  An arbitary sized NN can be specified 
together with any number of training operations which detail the 
training data and number of iterations etc.  The programme is up 
and working but I find that weight setting convergence is often 
hard to achieve as it requires lots of fine tuning of the training
data.  I suspect that my implementation of the back-propagation 
training method is some-what suspect especially the derivative of 
the threshold function ( T'(net) ).  

Could anyone out there please mail me some examples of back-
propagation source code ( C preferrably ) as I am sure that this
is a small problem.  Any other correspondence would be most
appreciated ( helpful or not ).

Thanx 

( Lets help make the world a smaller (and healthier) place )