Application of neuro-fuzzy inference system on wood identification
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.4028/www.scientific.net/AMM.590.667 http://hdl.handle.net/11449/227801 |
Resumo: | Searching for systems with intelligent, flexible, and self-adjusting solutions on imaging, which could provide the contraction of the human operators' presence, a range of techniques is found. Each one of them can control the process through the assistance of autonomous systems, either software or hardware. Therefore, modeling by traditional computational techniques is quite difficult, considering the complexity and non-linearity of image systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) behaves well as noise elimination and non-linear data treatment. Consequently, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, therefore, add value to the final product. Additionally, the Artificial Intelligence techniques, such as Neuro-Fuzzy Networks (NFN), have shown efficient, since they combine the ability to learn from examples and to generalize the learned information from the ANNs with the capacity of Fuzzy Logic, in order to transform linguistic variables in rules. Then, ANFIS plays active roles in an effort to reach a specific goal. © (2014) Trans Tech Publications, Switzerland. |
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Application of neuro-fuzzy inference system on wood identificationANFISArtificial neural networkImagingSearching for systems with intelligent, flexible, and self-adjusting solutions on imaging, which could provide the contraction of the human operators' presence, a range of techniques is found. Each one of them can control the process through the assistance of autonomous systems, either software or hardware. Therefore, modeling by traditional computational techniques is quite difficult, considering the complexity and non-linearity of image systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) behaves well as noise elimination and non-linear data treatment. Consequently, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, therefore, add value to the final product. Additionally, the Artificial Intelligence techniques, such as Neuro-Fuzzy Networks (NFN), have shown efficient, since they combine the ability to learn from examples and to generalize the learned information from the ANNs with the capacity of Fuzzy Logic, in order to transform linguistic variables in rules. Then, ANFIS plays active roles in an effort to reach a specific goal. © (2014) Trans Tech Publications, Switzerland.Universidade Estadual Paulista Júlio de Mesquita Filho, Rua Geraldo Alckmin, 519 Vila N. Sr. de Fátima, 18409-010 - Itapeva, SPUniversidade Estadual Paulista Júlio de Mesquita Filho, Av. Doutor Ariberto Pereira da Cunha. Portal das Colinas, 12516-410 - Guaratinguetá, SPUniversidade Estadual Paulista Júlio de Mesquita Filho, Rua Geraldo Alckmin, 519 Vila N. Sr. de Fátima, 18409-010 - Itapeva, SPUniversidade Estadual Paulista Júlio de Mesquita Filho, Av. Doutor Ariberto Pereira da Cunha. Portal das Colinas, 12516-410 - Guaratinguetá, SPUniversidade Estadual Paulista (UNESP)Vieira, Fábio Henrique Antunes [UNESP]Affonso, Carlos [UNESP]Alves, Manoel Cléber de Sampaio [UNESP]2022-04-29T07:20:14Z2022-04-29T07:20:14Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject667-671http://dx.doi.org/10.4028/www.scientific.net/AMM.590.667Applied Mechanics and Materials, v. 590, p. 667-671.1662-74821660-9336http://hdl.handle.net/11449/22780110.4028/www.scientific.net/AMM.590.6672-s2.0-84904297904Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengApplied Mechanics and Materialsinfo:eu-repo/semantics/openAccess2022-04-29T07:20:15Zoai:repositorio.unesp.br:11449/227801Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-29T07:20:15Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Application of neuro-fuzzy inference system on wood identification |
title |
Application of neuro-fuzzy inference system on wood identification |
spellingShingle |
Application of neuro-fuzzy inference system on wood identification Vieira, Fábio Henrique Antunes [UNESP] ANFIS Artificial neural network Imaging |
title_short |
Application of neuro-fuzzy inference system on wood identification |
title_full |
Application of neuro-fuzzy inference system on wood identification |
title_fullStr |
Application of neuro-fuzzy inference system on wood identification |
title_full_unstemmed |
Application of neuro-fuzzy inference system on wood identification |
title_sort |
Application of neuro-fuzzy inference system on wood identification |
author |
Vieira, Fábio Henrique Antunes [UNESP] |
author_facet |
Vieira, Fábio Henrique Antunes [UNESP] Affonso, Carlos [UNESP] Alves, Manoel Cléber de Sampaio [UNESP] |
author_role |
author |
author2 |
Affonso, Carlos [UNESP] Alves, Manoel Cléber de Sampaio [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Vieira, Fábio Henrique Antunes [UNESP] Affonso, Carlos [UNESP] Alves, Manoel Cléber de Sampaio [UNESP] |
dc.subject.por.fl_str_mv |
ANFIS Artificial neural network Imaging |
topic |
ANFIS Artificial neural network Imaging |
description |
Searching for systems with intelligent, flexible, and self-adjusting solutions on imaging, which could provide the contraction of the human operators' presence, a range of techniques is found. Each one of them can control the process through the assistance of autonomous systems, either software or hardware. Therefore, modeling by traditional computational techniques is quite difficult, considering the complexity and non-linearity of image systems. Compared to traditional models, the approach with Artificial Neural Networks (ANN) behaves well as noise elimination and non-linear data treatment. Consequently, the challenges in the wood industry justify the use of ANN as a tool for process improvement and, therefore, add value to the final product. Additionally, the Artificial Intelligence techniques, such as Neuro-Fuzzy Networks (NFN), have shown efficient, since they combine the ability to learn from examples and to generalize the learned information from the ANNs with the capacity of Fuzzy Logic, in order to transform linguistic variables in rules. Then, ANFIS plays active roles in an effort to reach a specific goal. © (2014) Trans Tech Publications, Switzerland. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-01 2022-04-29T07:20:14Z 2022-04-29T07:20:14Z |
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/10.4028/www.scientific.net/AMM.590.667 Applied Mechanics and Materials, v. 590, p. 667-671. 1662-7482 1660-9336 http://hdl.handle.net/11449/227801 10.4028/www.scientific.net/AMM.590.667 2-s2.0-84904297904 |
url |
http://dx.doi.org/10.4028/www.scientific.net/AMM.590.667 http://hdl.handle.net/11449/227801 |
identifier_str_mv |
Applied Mechanics and Materials, v. 590, p. 667-671. 1662-7482 1660-9336 10.4028/www.scientific.net/AMM.590.667 2-s2.0-84904297904 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Applied Mechanics and Materials |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
667-671 |
dc.source.none.fl_str_mv |
Scopus 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 |
|
_version_ |
1799965122865659904 |