Tech Report Announcement

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

The following tech report is available.  It is a substantially expanded
version of a paper of the same title that appeared in the proceedings of
the 1988 CMU Connectionist Models Summer School.


		   Learning State Space Trajectories
		      in Recurrent Neural Networks

			  Barak A. Pearlmutter

			       ABSTRACT

    We describe a number of procedures for finding $\partial E/\partial
    w_{ij}$ where $E$ is an error functional of the temporal trajectory
    of the states of a continuous recurrent network and $w_{ij}$ are the
    weights of that network.  Computing these quantities allows one to
    perform gradient descent in the weights to minimize $E$, so these
    procedures form the kernels of connectionist learning algorithms.
    Simulations in which networks are taught to move through limit
    cycles are shown.  We also describe a number of elaborations of the
    basic idea, such as mutable time delays and teacher forcing, and
    conclude with a complexity analysis.  This type of network seems
    particularly suited for temporally continuous domains, such as
    signal processing, control, and speech.


Overseas copies are sent first class so there is no need to make special
arrangements for rapid delivery.  Requests for copies should be sent to

        Catherine Copetas
        School of Computer Science
        Carnegie Mellon University
        Pittsburgh, PA  15213

or Copetas at CS.CMU.EDU by computer mail.  Ask for CMU-CS-88-191.