Abstract

[Chris Lacher]

             Backpropagation Learning in Expert Networks

                                 by

        R. C. Lacher, Susan I. Hruska, and David C. Kuncicky
                  Department of Computer Science
                     Florida State University


ABSTRACT.  Expert networks are event-driven, acyclic networks of neural objects
derived from expert systems.  The neural objects process information through a
non-linear combining function that is different from, and more complex than,
typical neural network node processors.  We develop backpropagation learning for
acyclic, event-driven nets in general and derive a specific algorithm for
learning in EMYCIN-derived expert networks.  The algorithm combines
backpropagation learning with other features of expert nets, including
calculation of gradients of the non-linear combining functions and the hypercube
nature of the knowledge space.  Results of testing the learning algorithm with a
medium-scale (97 node) expert network are presented.


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and the TITLE of the preprint to:    santan at nu.cs.fsu.edu


                                             --- Chris Lacher