dc.creator | Paluzo Hidalgo, Eduardo | es |
dc.creator | González Díaz, Rocío | es |
dc.creator | Gutiérrez Naranjo, Miguel Ángel | es |
dc.date.accessioned | 2023-04-03T11:06:24Z | |
dc.date.available | 2023-04-03T11:06:24Z | |
dc.date.issued | 2020-11 | |
dc.identifier.citation | 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. | |
dc.identifier.issn | 0893-6080 (impreso) | es |
dc.identifier.issn | 1879-2782 (online) | es |
dc.identifier.uri | https://hdl.handle.net/11441/143874 | |
dc.description.abstract | 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. | es |
dc.format | application/pdf | es |
dc.format.extent | 8 | es |
dc.language.iso | eng | es |
dc.publisher | ScienceDirect | es |
dc.relation.ispartof | Neural Networks, 131, 29-36. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Universal Approximation Theorem | es |
dc.subject | Simplicial Approximation Theorem | es |
dc.subject | Multi-layer feed-forward network | es |
dc.subject | Triangulations | es |
dc.title | Two-hidden-layer feed-forward networks are universal approximators: A constructive approach | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Matemática Aplicada I | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0893608020302628?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.neunet.2020.07.021 | es |
dc.journaltitle | Neural Networks | es |
dc.publication.volumen | 131 | es |
dc.publication.initialPage | 29 | es |
dc.publication.endPage | 36 | es |