Two reports available

[SANTINI@INGFI1.CINECA.IT]

The following two technical reports have been posted in the directory
/neural/papers of the ftp server aguirre.ingfi1.cineca.it (150.217.11.13):


            AN ALGORITHM FOR TRAINING NEURAL NETWORKS
               WITH ARBITRARY FEEDBACK STRUCTURE



         S. Santini(*)     A. Del Bimbo(*)   R. Jain(+)

         (*) Dipartimento di sistemi e Informatica,
             Universita' di Firenze, Firenze, Italy
         (+) Artificial Intelligence Lab., The University
             of Michigan, Ann Arbor, MI


                             Abstract

       In this  report, we  consider  multi-layer  discrete-time
       dynamic  networks with multiple unit-delay feedback paths
       between layers.  A block notation is introduced for  this
       class of networks which allows a great flexibility in the
       description of the network architecture and permits a
       unified treatment of static and dynamic networks. Networks
       are defined by recursively arranging blocks.
       Network blocks which satisfy certain *trainability* conditions
       can be embedded into other blocks through a set of
       *elementary connections*, so that the overall network still
       satisfies the same trainability conditions.
       The problem of training such a network is thus reduced to the
       definition of an algorithm ensuring the trainability of a
       block, assuming that all the embedded blocks are trainable.
       An algorithm is presented which is a block-matrix
       version of Forward Propagation, and is based on Werbos'
       ordered derivatives.


       This report is in the file:
       santini.feedback_NN_algorithm.ps.Z

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              SYSTEMS IN WHICH NOBODY KNOWS THE BIG PICTURE

                 S. Santini              A. Del Bimbo

                 Dipartimento di sistemi e Informatica,
                 Universita' di Firenze, Firenze, Italy

                              Abstract

         There is an understatement in engineering activities: if
         you have a problem, create a hierarchy. Hierarchical and
         centralized schemes are considered just ``the way things
         ought to be done''. In this paper, we briefly introduce
         the connectionist point of view, in which problems are
         solved by emergent computation, arising from local
         interaction of units, without any centralized control. In
         these systems, there is no knowledge of the overall problem,
         and in no place there is enough intelligence to ``understand''
         the problem.
         Yet, this kind of (dis)organization can actually solve
         problems and -- more important -- its properties can be
         mathematically analyzed.
         We present an example: a Neural Gas network that finds the
         minimum path between two points in an area were obstacles are
         present. We show that this global problem can be solved without
         any global organization. Moreover, we proof global properties
         of the emergent computation.


      This is in the file:
      santini.big_picture.ps.Z

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   HOW TO GET THE FILES:
   ---------------------

   % ftp aguirre.ingfi1.cineca.it (or 150.217.11.13)
   Connected to aguirre.ingfi1.cineca.it
   220 aguirre FTP server (SunOS 4.1) ready.
   Name: anonymous
   331 Guest login ok, send ident as password.
   Password: <Your ID>
   230 Guest login ok, access restrictions apply.
   ftp> cd neural/papers
   ftp  binary
   ftp> get santini.feedback_NN_algorithm.ps.Z
   ftp> get santini.feedback_NN_algorithm.ps.Z
   ftp> quit
   % uncompress santini.feedback_NN_algorithm.ps.Z
   % lpr santini.feedback_NN_algorithm.ps
   % uncompress santini.feedback_NN_algorithm.ps.Z
   % lpr santini.feedback_NN_algorithm.ps

   For any problem in retrieving files, or for any discussion and
   comment about their content, contact me at:

       santini at ingfi1.cineca.it


   Simone Santini