Paper in neuroprose

[ahg@eng.cam.ac.uk]

************** PLEASE DO NOT FORWARD TO OTHER NEWSGOUPS ****************

The following technical report has been placed in the neuroprose 
archives at Ohio State University:


                ALTERNATIVE ENERGY FUNCTIONS FOR
                   OPTIMIZING NEURAL NETWORKS

                  Andrew Gee and Richard Prager

	       Technical Report CUED/F-INFENG/TR 95

	               Cambridge University
		      Engineering Department 
		        Trumpington Street 
		        Cambridge CB2 1PZ 
			     England 


                            Abstract

When feedback neural networks are used to solve combinatorial
optimization problems, their dynamics perform some sort of descent on
a continuous energy function related to the objective of the discrete 
problem. For any particular discrete problem, there are generally a
number of suitable continuous energy functions, and the performance of
the network can be expected to depend heavily on the choice of such a
function. In this paper, alternative energy functions are employed to
modify the dynamics of the network in a predictable manner, and
progress is made towards identifying which are well suited to the
underlying discrete problems. This is based on a revealing study of a
large database of solved problems, in which the optimal solutions are
decomposed along the eigenvectors of the network's connection matrix.
It is demonstrated that there is a strong correlation between the mean
and variance of this decomposition and the ability of the network to
find good solutions. A consequence of this is that there may be some
problems which neural networks are not well adapted to solve,
irrespective of the manner in which the problems are mapped onto the
network for solution.

************************ How to obtain a copy ************************

a) Via FTP:

unix> ftp archive.cis.ohio-state.edu (or 128.146.8.52)
Name: anonymous
Password: neuron
ftp> cd pub/neuroprose
ftp> binary
ftp> get gee.energy_fns.ps.Z
ftp> quit
unix> uncompress gee.energy_fns.ps.Z
unix> lpr gee.energy_fns.ps (or however you print PostScript)

Please note that a couple of the figures in the paper were produced
on an Apple Mac, and the resulting PostScript is not quite standard.
People using an Apple LaserWriter should have no problems though.

b) Via postal mail:

Request a hardcopy from

Andrew Gee,
Speech Laboratory,
Cambridge University Engineering Department, 
Trumpington Street, 
Cambridge CB2 1PZ,
England.

or email me: ahg at eng.cam.ac.uk