Rules and Neural Networks. Paper announcement.

[MFMISMIC%HMARL5.BITNET@vma.cc.cmu.edu]

The following paper has been published in the proceedings of the
European Simulation Multiconference 91 in Copenhagen:

                HOMOMORPHIC TRANSFORMATION FROM
                 NEURAL NETWORKS TO RULE BASES

Author: Michael Egmont-Petersen,
        Computer Resources International a/s
                       and
        Copenhagen Business School

Abstract: In this article a method to extract the knowl-
edge induced in a neural network is presented. The
method explicates the relation between a network's
inputs and its outputs. This relation is stored as logic
rules. The feasibility of the method is studied by means
of three test examples. The result is that the method
can be used, though some drawbacks are detected. One
is that the method sometimes generates a lot of rules.
For fast retrieval, these rules can well be stored in a B-
tree.

Contents:

1. Introduction
2. Synthesizing Rule Bases Parsimoniously
3. Description of the Experiments
4. Practical Applicability of the Algorithm
5. Conclusion

Hardcopies of the paper are avaliable. Please send requests to the
following address in Holland:

  Institute of Medical Statistics and Informatics
  University of Limburg
  Postbus 616
  NL-6200 MD Maastricht
  The Netherlands

  att. Michael Egmont-Petersen


Michael Egmont-Petersen