Artículo
Two-hidden-layer feed-forward networks are universal approximators: A constructive approach
Autor/es | Paluzo Hidalgo, Eduardo
González Díaz, Rocío Gutiérrez Naranjo, Miguel Ángel |
Departamento | Universidad de Sevilla. Departamento de Matemática Aplicada I Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2020-11 |
Fecha de depósito | 2023-04-03 |
Publicado en |
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Resumen | It is well-known that artificial neural networks are universal approximators. The classical existence result proves that, given a continuous function on a compact set embedded in an n-dimensional space, there exists a ... It is well-known that artificial neural networks are universal approximators. The classical existence result proves that, given a continuous function on a compact set embedded in an n-dimensional space, there exists a one-hidden-layer feed-forward network that approximates the function. In this paper, a constructive approach to this problem is given for the case of a continuous function on triangulated spaces. Once a triangulation of the space is given, a two-hidden-layer feed-forward network with a concrete set of weights is computed. The level of the approximation depends on the refinement of the triangulation. |
Cita | Paluzo Hidalgo, E., González Díaz, R. y Gutiérrez Naranjo, M.Á. (2020). Two-hidden-layer feed-forward networks are universal approximators: A constructive approach. Neural Networks, 131, 29-36. https://doi.org/10.1016/j.neunet.2020.07.021. |
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