Path: utzoo!attcan!uunet!ginosko!rex!ck
From: ck@rex.cs.tulane.edu (Cris Koutsougeras)
Newsgroups: comp.ai.neural-nets
Subject: Re : Step Function
Message-ID: <1060@rex.cs.tulane.edu>
Date: 23 Aug 89 09:19:37 GMT
Reply-To: ck@rex.cs.tulane.edu (Cris Koutsougeras)
Organization: Computer Science Dept., Tulane Univ., New Orleans, LA
Lines: 25

>I am trying to teach a network to accept as input a step function
>and return as output the same step function or a scaled version of the
>same. I have tried single and multiple inputs and outputs with
>multiple hidden layers for my network..but am having no luck 
>whatsoever...I am using back propogation learning and my hidden
>layers use the sigmoidal function.
>
>if anybody has any suggestions to make please post reply on net
>or send mail to above address.
>
>Neural Network Group
>Chemical Engineering Department
>University of Texas at Austin
> 

With the back prop only an analytic (continous differentiable) can be learned.
The perfect step function is not therefore it will not be learned. A close
approximation can be learned if your training set has enough samples around
the point of the jump and of course quite enough coverage of the rest of the
total interval. Look at it from an other point of view. No matter how many 
units (nodes) you put in your net, you are never going to have enough 
non-linearity to match the perfect step function. If you want I can send you
a paper on some related experiments from our group at Tulane U.

C. Koutsougeras