Refereed full papers (journals, book chapters, international conferences)

1993

@article{Bul1993,
	vgclass =	{refpap},
	vgproject =	{nn},
	author =	{A. Bulsari},
	title =	{Some Analytical Solutions to the General Approximation
	Problem for Feedforward Neural Networks},
	journal =	{Neural Networks},
	volume =	{6},
	number =	{7},
	pages =	{991--996},
	year =	{1993},
	abstract =	{The general approximation problem of interest to the area
	of feedforward neural networks is stated. Solutions for some special
	cases are given, which include an upper bound on the number of nodes in
	hidden layer(s) and the weights for that configuration. Analytical
	solutions to the general feedforward neural network problem in
	one-dimensional cases requiring an infinite number of nodes are
	presented. The practical solutions (not requiring an infinite number of
	nodes) in one-dimensional cases are derived under piecewise constant
	approximations with constant width partitions, under piecewise constant
	spproximations with variable width partitions, and under piecewise
	linear approximations using ramps instead of sigmoids. A four layer
	solution to the general feedforward neural network problem in the
	n-dimensional case is presented. A three layer solution to the general
	feedforward neural network problem in the n-dimensional case with
	piecewise constant spproximation requires the use of the corner
	function as the activation function. The corner function, a special
	case of n dimensional sigmoid function, is found to have desirable
	characteristics, and can be used to approximate functions with much
	weaker requirements (only boundedness and piecewise continuity.)
	Concave regions can be formed with a single layer of nodes with the
	corner function.},
}