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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

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BibTeXRIS

Larry M. Manevitz, Moshe Shoham, Mark Meltser. 1996. Approximating Functions by Neural Networks: A Constructive Solution in the Uniform Norm.. https://doi.org/10.1016/0893-6080(95)00124-7

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