Technical Reports

1992

@techreport{GeP1992,
	vgclass =	{report},
	vgproject =	{nn},
	author =	{A.H. Gee and R. W. Prager},
	title =	{Alternative Energy Functions for Optimizing Neural
	Networks},
	number =	{CUED/F-INFENG/TR 95},
	institution =	{Cambridge University Engineering Department},
	address =	{Trumpington St., Cambridge CB2 1PZ, England},
	month =	{March},
	year =	{1992},
	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 string 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.},
}