Comparative study between RBF and radial-PPS neural networks
Autor(a) principal: | |
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Data de Publicação: | 1998 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1117/12.304830 http://hdl.handle.net/11449/8272 |
Resumo: | The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques. |
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Repositório Institucional da UNESP |
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Comparative study between RBF and radial-PPS neural networksPPS-waveletsneural networksfunction approximationwavelet transformThe study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.Univ Estadual Paulista, Dept Comp Sci, Bauru, SP, BrazilUniv Estadual Paulista, Dept Comp Sci, Bauru, SP, BrazilSpie - Int Soc Optical EngineeringUniversidade Estadual Paulista (Unesp)Marar, João Fernando [UNESP]Carvalho, ECBdos Santos, J. D.2014-05-20T13:25:55Z2014-05-20T13:25:55Z1998-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject593-602http://dx.doi.org/10.1117/12.304830Applications and Science of Computational Intelligence. Bellingham: Spie-int Soc Optical Engineering, v. 3390, p. 593-602, 1998.0277-786Xhttp://hdl.handle.net/11449/827210.1117/12.304830WOS:0000734526000611233049484488761Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplications and Science of Computational Intelligenceinfo:eu-repo/semantics/openAccess2024-04-23T16:11:19Zoai:repositorio.unesp.br:11449/8272Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:19Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Comparative study between RBF and radial-PPS neural networks |
title |
Comparative study between RBF and radial-PPS neural networks |
spellingShingle |
Comparative study between RBF and radial-PPS neural networks Marar, João Fernando [UNESP] PPS-wavelets neural networks function approximation wavelet transform |
title_short |
Comparative study between RBF and radial-PPS neural networks |
title_full |
Comparative study between RBF and radial-PPS neural networks |
title_fullStr |
Comparative study between RBF and radial-PPS neural networks |
title_full_unstemmed |
Comparative study between RBF and radial-PPS neural networks |
title_sort |
Comparative study between RBF and radial-PPS neural networks |
author |
Marar, João Fernando [UNESP] |
author_facet |
Marar, João Fernando [UNESP] Carvalho, ECB dos Santos, J. D. |
author_role |
author |
author2 |
Carvalho, ECB dos Santos, J. D. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Marar, João Fernando [UNESP] Carvalho, ECB dos Santos, J. D. |
dc.subject.por.fl_str_mv |
PPS-wavelets neural networks function approximation wavelet transform |
topic |
PPS-wavelets neural networks function approximation wavelet transform |
description |
The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques. |
publishDate |
1998 |
dc.date.none.fl_str_mv |
1998-01-01 2014-05-20T13:25:55Z 2014-05-20T13:25:55Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1117/12.304830 Applications and Science of Computational Intelligence. Bellingham: Spie-int Soc Optical Engineering, v. 3390, p. 593-602, 1998. 0277-786X http://hdl.handle.net/11449/8272 10.1117/12.304830 WOS:000073452600061 1233049484488761 |
url |
http://dx.doi.org/10.1117/12.304830 http://hdl.handle.net/11449/8272 |
identifier_str_mv |
Applications and Science of Computational Intelligence. Bellingham: Spie-int Soc Optical Engineering, v. 3390, p. 593-602, 1998. 0277-786X 10.1117/12.304830 WOS:000073452600061 1233049484488761 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Applications and Science of Computational Intelligence |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
593-602 |
dc.publisher.none.fl_str_mv |
Spie - Int Soc Optical Engineering |
publisher.none.fl_str_mv |
Spie - Int Soc Optical Engineering |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1797789668022943744 |