Tech Report Available

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The following technical report is available by FTP:


             A Synthesis of Gradient and Hamiltonian Dynamics 
                 Applied to Learning in Neural Networks

                  James W. Howse,  Chaouki T. Abdallah 
                        and Gregory L. Heileman


                              Abstract

The process of model learning can be considered in two stages: model selection
and parameter estimation.  In this paper a technique is presented for
constructing dynamical systems with desired qualitative properties.  The
approach is based on the fact that an n-dimensional nonlinear dynamical
system can be decomposed into one gradient and (n - 1) Hamiltonian
systems.  Thus, the model selection stage consists of choosing the gradient
and Hamiltonian portions appropriately so that a certain behavior is
obtainable.  To estimate the parameters, a stably convergent learning rule is
presented.  This algorithm is proven to converge to the desired system
trajectory for all initial conditions and system inputs.  This technique can
be used to design neural network models which are guaranteed to solve certain
classes of nonlinear identification problems.


Retrieval:  FTP anonymous to:
   ftp.eece.unm.edu
   cd howse
   get techrep.ps.gz


This is a PostScript file compressed with gzip.  The paper is 28 pages long
and formatted to print DOUBLE-sided.  This paper has been submitted for
publication.  If there are any retrieval problems please let me know.  I would
welcome any comments or suggestions regarding the paper.

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  James Howse - howse at eece.unm.edu
   __  __  __  __   _    _
  /\ \/\ \/\ \/\ \/\ `\_/ `\   University of New Mexico
  \ \ \ \ \ \ `\\ \ \       \   Department of EECE, 224D
   \ \ \ \ \ \ , ` \ \ `\_/\ \   Albuquerque, NM 87131-1356
    \ \ \_\ \ \ \`\ \ \ \_',\ \   Telephone: (505) 277-0805
     \ \_____\ \_\ \_\ \_\ \ \_\   FAX: (505) 277-1413 or (505) 277-1439
      \/_____/\/_/\/_/\/_/  \/_/

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