title: Approximating Functions by Neural Networks: A Constructive Solution in the Uniform Norm.
abstract: A method for constructively approximating functions in the uniform (i.e., maximal error) norm by successive changes in the weights and number of neurons in a neural network is developed. This is a realization of the approximation results of Cybenko, Hecht-Nielsen, Hornik, Stinchcombe, White, Gallant, Funahashi, Leshno et al., and others. The constructive approximation in the uniform norm is more appropriate for a number of examples, such as robotic arm motion, and stands in contrast with more standard methods, such as back-propagation, which approximate only in the average error norm. Copyright 1996 Elsevier Science Ltd
authors.affiliation: University of Haifa, Haifa, Israel
authors.forename: Larry M.
authors.initials: LM
authors.lastname: Manevitz
authors.forename: Moshe
authors.initials: M
authors.lastname: Shoham
authors.forename: Mark
authors.initials: M
authors.lastname: Meltser