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From: al@gtx.com (Alan Filipski)
Newsgroups: comp.ai.neural-nets
Subject: Re: back-prop NNs and `SAS' regression!
Message-ID: <1155@gtx.com>
Date: 27 Dec 89 15:24:52 GMT
References: <220700005@uxe.cso.uiuc.edu> <1989Dec18.210859.23621@wuche2.wustl.edu> <21539@uflorida.cis.ufl.EDU> <1989Dec19.172314.16051@wuche2.wustl.edu> <21541@uflorida.cis.ufl.EDU>
Reply-To: al@gtx.UUCP (Alan Filipski)
Organization: GTX Corporation, Phoenix
Lines: 16

In article <21541@uflorida.cis.ufl.EDU> fishwick@fish.cis.ufl.edu (Paul Fishwick) writes:
>                           I think that is important that we always
>remember that a neural network, like a signal flow graph, is just
>a convenient representation for a set of equations
>

Depends on your point of view.  One might just as well say that a set
of equations is just a convenient representation for a Neural Network.
I don't see that one representation is necessarily more fundamental
than the other.


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