Limitations of the approximation capabilities of neural networks with one hidden layer
- 1 December 1996
- journal article
- Published by Springer Nature in Advances in Computational Mathematics
- Vol. 5 (1) , 233-243
- https://doi.org/10.1007/bf02124745
Abstract
No abstract availableKeywords
This publication has 15 references indexed in Scilit:
- Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networksPublished by Elsevier ,2003
- Neural Networks for Optimal Approximation of Smooth and Analytic FunctionsNeural Computation, 1996
- Regularization Theory and Neural Networks ArchitecturesNeural Computation, 1995
- Neural networks for localized approximationMathematics of Computation, 1994
- Universal approximation bounds for superpositions of a sigmoidal functionIEEE Transactions on Information Theory, 1993
- Approximation properties of a multilayered feedforward artificial neural networkAdvances in Computational Mathematics, 1993
- Multilayer feedforward networks with a nonpolynomial activation function can approximate any functionNeural Networks, 1993
- Approximation by superpositions of a sigmoidal functionMathematics of Control, Signals, and Systems, 1989
- Optimal nonlinear approximationmanuscripta mathematica, 1989
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989