No subject

[Martin Roescheisen]

Technical Report available

INCORPORATING PRIOR KNOWLEDGE IN
PARSIMONIOUS NETWORKS OF LOCALLY-TUNED UNITS

Keywords: Gaussian Units (Moody, Poggio), higher-dimensionality,
rolling mills, use of prior knowledge.

Reimar Hofmann 
Munich Technical University

Martin R\"oscheisen
Munich Technical University

Volker Tresp
Siemens AG
Corporate Research and Development


Abstract:

Utilizing  Bayes decision theory, we develop a theoretical
foundation for a localized network architecture that requires
few centers to be allocated and can therefore be employed in problems
which because of  their high input dimensionality  could not  yet be tackled
by such networks.
We show how quantitative {\it a priori} knowledge can be readily 
incorporated by choosing a specific training regime.
The network was employed as a neural controller for a  hot line rolling mill
and achieved in this application one to two orders of magnitude higher 
accuracy than optimally-tuned standard algorithms such as sigmoidal 
backpropagation and performed significantly better than  a state-of-the-art 
analytic model.

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Hardcopies of the full paper can be obtained
by sending e-mail to hofmannr at lan.informatik.tu-muenchen.dbp.de