paper available

[Christian Omlin]

The following paper is available on the website
  
  http://www.neci.nj.nec.com/homepages/omlin/omlin.html 

The paper gives an overview of our work and contains an
extensive bibliography on the representation of discrete
dynamical systems in recurrent neural networks.

 -Christian

===================================================================

       Learning, Representation, and Synthesis of 
               Discrete Dynamical Systems
        in Continuous Recurrent Neural Networks (*)

      C. Lee Giles (a,b)  and  Christian W. Omlin (a)

                (a) NEC Research Institute
                      4 Independence Way
                      Princeton, NJ 08540

	 (b) Institute for Advanced Computer Studies
                    University of Maryland
                    College Park, MD 20742



		          ABSTRACT


This paper gives an overview on learning  and  representation  of
discrete-time, discrete-space dynamical systems in discrete-time,
continuous-space  recurrent  neural  networks.   We   limit   our
discussion to dynamical systems (recurrent neural networks) which
can be represented as finite-state machines (e.g. discrete  event
systems   ).   In   particular,   we   discuss   how  a  symbolic
representation  of  the  learned  states  and  dynamics  can   be
extracted from trained neural networks, and how (partially) known
deterministic finite-state automata  (DFAs)  can  be  encoded  in
recurrent  networks.   While the DFAs that can be learned exactly
with recurrent neural networks are generally small (on the  order
of  20  states), there exist subclasses of DFAs with on the order
of 1000 states that can be learned by small  recurrent  networks.
However,  recent work in natural language processing implies that
recurrent networks can possibly learn larger state systems.


(*) Appeared in Proceedings of the IEEE Workshop on Architectures
for  Semiotic  Modeling  and  Situation Analysis in Large Complex
Systems, Monterey, CA, August 27-29, 1995. Copyright IEEE Press.