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From: fishwick@fish.cis.ufl.edu (Paul Fishwick)
Newsgroups: comp.ai.neural-nets
Subject: Re: back-prop NNs and `SAS' regression!
Message-ID: <21539@uflorida.cis.ufl.EDU>
Date: 19 Dec 89 15:25:28 GMT
References: <220700005@uxe.cso.uiuc.edu> <1989Dec18.210859.23621@wuche2.wustl.edu>
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Reply-To: fishwick@fish.cis.ufl.edu (Paul Fishwick)
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In article <1989Dec18.210859.23621@wuche2.wustl.edu> joshi@wuche2.UUCP (Amol Joshi) writes:
>In article <220700005@uxe.cso.uiuc.edu> kbesrl@uxe.cso.uiuc.edu writes:
>>
>>
>>I have been experimenting with back-prop neural nets for the past
>>few months. I find that they are only as good as polynomial
>>regression. Actually, I ran a back-prop neural net on some
>>continuous mapping problems and found that they achieved the
>>same performance as the `SAS' statistical package.
>>
>	the techniques of multi-variable analysis are not suited to non-linear
>	phenomena and many real problems are non-linear.
>	even though non-linear regressions can treat non-linear phenomena, they
>	require that the structure of the math model be prefixed.
>	it is in these cases that backprop nets would be more useful.

You say that in regression that the "structure of the model be prefixed"
however I will debate this assumption -- the structure of a set of
equations is no more prefixed than a neural network model. A neural
network is a set of equations shown in a graphical syntactic form.
It is just as easy to add and delete terms/equations as it is to add/delete
nodes, etc. The equational equivalent of removing a link is to make
zero a parameter.

We have also done some work on using neural networks for purposes of
simulation and time-series analysis (vs. the Box-Jenkins methodology).
Much more comparative work is necessary!! Here is a recent article:

Fishwick, P. A. "Neural Network Models in Simulation: A Comparision
with Traditional Modelling Appoaches", Winter Simulation Conference,
December 1989, Washington, D.C., pp. 702 - 710.>

-paul f.

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