tr announcement: CMU-CS-90-196

[Barak.Pearlmutter@F.GP.CS.CMU.EDU]

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The following 30 page technical report is now available.  It can be
FTPed from the neuroprose archives at OSU, under the name
pearlmutter.dynets.ps.Z, as shown below, which is the preferred mode of
acquisition, or can be ordered by sending a note to

	School of Computer Science
	Carnegie Mellon University
	Pittsburgh, PA  15213
	USA

along with a check for $2 (domestic) or $5 (outside the USA) to help
defray the expense of reproduction and mailing.
----------------

		   Dynamic Recurrent Neural Networks

			  Barak A. Pearlmutter

			     December 1990

			     CMU-CS-90-196

		       (supersedes CMU-CS-88-191)

	We a survey learning algorithms for recurrent neural networks
	with hidden units and attempt to put the various techniques into
	a common framework.  We discuss fixpoint learning algorithms,
	namely recurrent backpropagation and deterministic Boltzmann
	Machines, and non-fixpoint algorithms, namely backpropagation
	through time, Elman's history cutoff nets, and Jordan's output
	feedback architecture.  Forward propagation, an online technique
	that uses adjoint equations, is also discussed.  In many cases,
	the unified presentation leads to generalizations of various
	sorts.  Some simulations are presented, and at the end, issues
	of computational complexity are addressed.

----------------
FTP Instructions:

ftp cheops.cis.ohio-state.edu  (or ftp 128.146.8.62)
Name: anonymous
Password: state-your-name-please
ftp> cd pub/neuroprose
ftp> get pearlmutter.dynets.ps.Z
    300374 bytes sent in 9.9 seconds (26 Kbytes/s)
ftp> quit
unix> zcat pearlmutter.dynets.ps.Z | lpr

Unlike some files in the archive, the postscript file has been tested
and will print properly on printers without much memory.