Preprint: Neural net architectures for temporal sequence processing

[Michael C. Mozer]

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Neural net architectures for temporal sequence processing

Michael C. Mozer
Department of Computer Science 
University of Colorado


I present a general taxonomy of neural net architectures for processing
time-varying patterns.  This taxonomy subsumes many existing architectures in
the literature, and points to several promising architectures that have yet
to be examined.  Any architecture that processes time-varying patterns
requires two conceptually distinct components:  a short-term memory that holds
on to relevant past events and an associator that uses the short-term memory
to classify or predict.  The taxonomy is based on a characterization of
short-term memory models along the dimensions of form, content, and
adaptability.  Experiments on predicting future values of a financial time
series (US dollar-Swiss franc exchange rates) are presented using several
alternative memory models.  The results of these experiments serve as a
baseline against which more sophisticated architectures can be compared.


To appear in:  A. S. Weigend & N. A. Gershenfeld (Eds.), _Predicting the future
and understanding the past_.  Redwood City, CA: Addison-Wesley.  Spring 1993.


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To retrieve:

   unix> ftp archive.cis.ohio-state.edu
   Name: anonymous 
   230 Guest ogin ok, access restrictions apply.
   ftp> cd pub/neuroprose
   ftp> binary
   ftp> get mozer.architectures.ps.Z
   200 PORT command successful.
   ftp> quit
   unix> zcat mozer.architectures.ps.Z | lpr

Warning:  May not print on wimpy laser printers.