SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions

Autores
Giordano, Pablo César; Goicoechea, Hector Casimiro; Olivieri, Alejandro Cesar
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
SRO_ANN, a MatLab® toolbox for implementing multiple surface response optimization by artificial neural networks (SRO_ANN) is presented. Radial basis functions, a type of artificial neural networks, are applied through an easily managed graphical user interface. A detailed description of the interface is provided, including a simulated and two literature examples which allow one to show the potentiality of the software. The discussed experimental examples correspond to: (1) the maximization of the research octane number (RON) of fuels, influenced by three factors (reaction temperature, operating pressure and low liquid hourly space velocity), and (2) the optimization of the calcification process for diced tomatoes, evaluated through three different responses (calcium content, firmness and pH), which are affected by three factors (calcium concentration, solution temperature and treatment time). The results show that the application of a nonparametric tool can enhance the performance of optimization modeling tasks.
Fil: Giordano, Pablo César. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Fil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
Materia
Artificial Neural Networks (Ann)
Desirability Function
Response Surface Methodology (Rsm)
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/58594
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/58594
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functionsGiordano, Pablo CésarGoicoechea, Hector CasimiroOlivieri, Alejandro CesarArtificial Neural Networks (Ann)Desirability FunctionResponse Surface Methodology (Rsm)https://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1SRO_ANN, a MatLab® toolbox for implementing multiple surface response optimization by artificial neural networks (SRO_ANN) is presented. Radial basis functions, a type of artificial neural networks, are applied through an easily managed graphical user interface. A detailed description of the interface is provided, including a simulated and two literature examples which allow one to show the potentiality of the software. The discussed experimental examples correspond to: (1) the maximization of the research octane number (RON) of fuels, influenced by three factors (reaction temperature, operating pressure and low liquid hourly space velocity), and (2) the optimization of the calcification process for diced tomatoes, evaluated through three different responses (calcium content, firmness and pH), which are affected by three factors (calcium concentration, solution temperature and treatment time). The results show that the application of a nonparametric tool can enhance the performance of optimization modeling tasks.Fil: Giordano, Pablo César. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; ArgentinaFil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; ArgentinaFil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaElsevier Science2017-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/58594Giordano, Pablo César; Goicoechea, Hector Casimiro; Olivieri, Alejandro Cesar; SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 171; 12-2017; 198-2060169-7439CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2017.11.004info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S016974391730401Xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:02:13Zoai:ri.conicet.gov.ar:11336/58594instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:02:13.282CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions
title SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions
spellingShingle SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions
Giordano, Pablo César
Artificial Neural Networks (Ann)
Desirability Function
Response Surface Methodology (Rsm)
title_short SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions
title_full SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions
title_fullStr SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions
title_full_unstemmed SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions
title_sort SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions
dc.creator.none.fl_str_mv Giordano, Pablo César
Goicoechea, Hector Casimiro
Olivieri, Alejandro Cesar
author Giordano, Pablo César
author_facet Giordano, Pablo César
Goicoechea, Hector Casimiro
Olivieri, Alejandro Cesar
author_role author
author2 Goicoechea, Hector Casimiro
Olivieri, Alejandro Cesar
author2_role author
author
dc.subject.none.fl_str_mv Artificial Neural Networks (Ann)
Desirability Function
Response Surface Methodology (Rsm)
topic Artificial Neural Networks (Ann)
Desirability Function
Response Surface Methodology (Rsm)
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv SRO_ANN, a MatLab® toolbox for implementing multiple surface response optimization by artificial neural networks (SRO_ANN) is presented. Radial basis functions, a type of artificial neural networks, are applied through an easily managed graphical user interface. A detailed description of the interface is provided, including a simulated and two literature examples which allow one to show the potentiality of the software. The discussed experimental examples correspond to: (1) the maximization of the research octane number (RON) of fuels, influenced by three factors (reaction temperature, operating pressure and low liquid hourly space velocity), and (2) the optimization of the calcification process for diced tomatoes, evaluated through three different responses (calcium content, firmness and pH), which are affected by three factors (calcium concentration, solution temperature and treatment time). The results show that the application of a nonparametric tool can enhance the performance of optimization modeling tasks.
Fil: Giordano, Pablo César. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Fil: Goicoechea, Hector Casimiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina
Fil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina
description SRO_ANN, a MatLab® toolbox for implementing multiple surface response optimization by artificial neural networks (SRO_ANN) is presented. Radial basis functions, a type of artificial neural networks, are applied through an easily managed graphical user interface. A detailed description of the interface is provided, including a simulated and two literature examples which allow one to show the potentiality of the software. The discussed experimental examples correspond to: (1) the maximization of the research octane number (RON) of fuels, influenced by three factors (reaction temperature, operating pressure and low liquid hourly space velocity), and (2) the optimization of the calcification process for diced tomatoes, evaluated through three different responses (calcium content, firmness and pH), which are affected by three factors (calcium concentration, solution temperature and treatment time). The results show that the application of a nonparametric tool can enhance the performance of optimization modeling tasks.
publishDate 2017
dc.date.none.fl_str_mv 2017-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/58594
Giordano, Pablo César; Goicoechea, Hector Casimiro; Olivieri, Alejandro Cesar; SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 171; 12-2017; 198-206
0169-7439
CONICET Digital
CONICET
url http://hdl.handle.net/11336/58594
identifier_str_mv Giordano, Pablo César; Goicoechea, Hector Casimiro; Olivieri, Alejandro Cesar; SRO_ANN: An integrated MatLab toolbox for multiple surface response optimization using radial basis functions; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 171; 12-2017; 198-206
0169-7439
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2017.11.004
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S016974391730401X
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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