Paper & software available on modified cascor.

[E. Fiesler]

Paper available
----------------

The following paper has been published in Proc. Neuro-Nimes '92, Nimes,
France, November 1992, pp. 455-466. This paper is available at the IDIAP
ftp site.  Instructions for obtaining a copy of this paper are given at
the end of this message.

-----------------------------------------------------------------------

    Variations on the Cascade-Correlation Learning Architecture
              for Fast Convergence in Robot Control


	   Natalio Simon  Henk Corporaal  Eugene Kerckhoffs
	   Delft University of Technology, The Netherlands


Abstract
--------
Most applications of Neural Networks in Control Systems use a version of
the Back-Propagation algorithm for training.  Learning in these networks
is generally a slow and very time consuming process.
Cascade-Correlation is a supervised learning algorithm that
automatically determines the size and topology of the network and
is quicker than back-propagation in learning for several benchmarks.
We present a modified version of the Cascade-Correlation learning
algorithm, which is used to implement the inverse kinematic 
transformations of a robot arm controller with two and three degrees of 
freedom.  This new version shows faster convergence than the original 
and scales better to bigger training sets and lower tolerances.

=========================================================================



Public Domain Code available
----------------------------

The code of the modified cascade-correlation learning architecture,
presented in the above report, is also available at the IDIAP ftp site. 
Instructions for obtaining a copy of this software are given at the
end of this message.  A description of the code follows:

/********************************************************************************/
/* C implementation of the Modified Cascade-Correlation learning algorithm	*/
/*										*/
/*   Modified by:         N. Simon						*/
/*                        Department of Electrical Engineering			*/
/*                        Computer Architecture and Digital Systems		*/
/*                        Delft University of Technology			*/
/*                        2600 GA Delft, The Netherlands			*/
/*										*/
/*                        E-mail:  natalio at zen.et.tudelft.nl			*/
/*										*/
/*										*/
/* This code is a modification of the original code written by R. Scott		*/
/* Crowder of Carnegie Mellon University (version 1.32).			*/
/* That code is a port to C from the original Common Lisp implementation	*/
/* written by Scott E. Fahlman.  (Version dated June 1 1990.)			*/
/*										*//*										*/
/* For an explanation of the original algorithm, see "The			*/
/* Cascade-Correlation Learning Architecture" by Scott E. Fahlman and		*/
/* Christian Lebiere in D. S. Touretzky (ed.), "Advances in Neural		*/
/* Information Processing Systems 2", Morgan Kaufmann, 1990.  A somewhat	*/
/* longer version is available as CMU Computer Science Tech Report		*/
/* CMU-CS-90-100.								*/
/*										*/
/* For an explanation of the Modified Cascade-Correlation learning		*/
/* see "Variations on the Cascade-Correlation Learning Architecture for		*/
/* Fast Convergence in Robot Control" by N. Simon, H. Corporaal and		*/
/* E. Kerckhoffs, in Proc. Neuro-Nimes '92, Nimes,  France, 1992,		*/
/* pp. 455-466.									*/
/********************************************************************************/


Instructions for obtaining a copy of the paper:

unix>     ftp Maya.IDIAP.CH (or: ftp 192.33.221.1)
login:    anonymous
password: <your e-mail address>
ftp>      cd pub/papers/neural
ftp>      binary
ftp>      get simon.variations.ps.Z
ftp>      bye
unix>     zcat simon.variations.ps.Z | lpr
                  (or however you uncompress and print a postscript file)



Instructions for obtaining a copy of the software:

unix>     ftp Maya.IDIAP.CH (or: ftp 192.33.221.1)
login:    anonymous
password: <your e-mail address>
ftp>      cd pub/software/neural
ftp>      binary
ftp>      get mcascor.c.Z
ftp>      bye
unix>     uncompress mcascor.c.Z


				E. Fiesler
				IDIAP