paper available: learning attractors

[Fu-Sheng Tsung]

The following paper has been placed in the Neuroprose archive.
Comments and questions are welcome.

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           Phase-Space learning for recurrent networks
                                
             Fu-Sheng Tsung and Garrison W Cottrell
			    0114 CSE
			  UC San Diego
		      La Jolla, CA 92093

                            abstract:

We   study  the  problem  of  learning  nonstatic  attractors  in
recurrent networks. With concepts from dynamical systems  theory,
we  show  that this problem can be reduced to three sub-problems,
(a) that of embedding the temporal trajectory in phase space, (b)
approximating   the  local  vector  field,   and   (c)   function
approximation using feedforward networks. This general  framework
overcomes  problems with traditional methods  by  providing  more
appropriate  error gradients and enforcing stability  explicitly.
We describe an online version of our method we call ARTISTE, that
can learn periodic attractors without teach-forcing.

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Thanks to Jordan Pollack for providing this service, despite being the
father of a new baby boy!

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FTP INSTRUCTIONS

"Getps tsung.phase.ps.Z" if you have the shell script, or

     unix% ftp archive.cis.ohio-state.edu (or 128.146.8.52)
     Name: anonymous
     Password: neuron
     ftp> cd pub/neuroprose
     ftp> binary
     ftp> get tsung.phase.ps.Z
     ftp> bye
     unix% zcat tsung.phase.ps.Z | lpr
(the paper is 18 pages)
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