Approximating Functions by Neural Networks: A Constructive Solution in the Uniform Norm

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...

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Veröffentlicht in:	Neural networks 1996-08, Vol.9 (6), p.965-978
Hauptverfasser:	Meltser, Mark, Shoham, Moshe, Manevitz, Larry M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Approximating functions Artificial intelligence Artificial neural networks Computer science control theory systems Connectionism. Neural networks Constructive approximation Dynamic neural network architecture Exact sciences and technology Feed-forward Uniform norm
Online-Zugang:	Volltext
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