Search Contact information
University of Cambridge Home Department of Engineering
University of Cambridge > Engineering Department > Machine Intelligence Lab

Abstract for ayestaran_tr137

Cambridge University Engineering Department Technical Report CUED/F-INFENG/TR137

THE LOGICAL GATES GROWING NETWORK

Horacio Ayestaran and Richard Prager

July 1993

A modular three layer variable structure feedforward network capable of learning by stages is proposed. It consists of a first layer of threshold units, and two subsequent layers of logical gates. The threshold units have a vectorial threshold (instead of a simple scalar), which gives them a spatial reference within the input space. They are trained using the method ofcentroids, developed by the authors. The modularity of this arrangement allows progressive learning, and the extra units are added as needed, to match the complexity of the problem. The system was tested both with real data and with artificially generated data, to assess its potential. Finally, ways of expanding on the present model are discussed.


(ftp:) ayestaran_tr137.ps.Z (http:) ayestaran_tr137.ps.Z
PDF (automatically generated from original PostScript document - may be badly aliased on screen):
  (ftp:) ayestaran_tr137.pdf | (http:) ayestaran_tr137.pdf

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.

© 2005 Cambridge University Engineering Dept
Information provided by milab-maintainer