Genetic programming and bacterial algorithm for neural networks and fuzzy systems design
Autor(a) principal: | |
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Data de Publicação: | 2003 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/50 |
Resumo: | In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use. |
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Genetic programming and bacterial algorithm for neural networks and fuzzy systems designControlo automáticoRedes neuronaisSistemas fuzzyProgramação genéticaAlgoritmo bacteriano681.5Constructive algorithmsB-splinesGenetic programmingBacterial evolutionary algorithmFuzzy rule baseIn the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.FaroSapientiaCabrita, Cristiano LourençoBotzheim, J.Ruano, AntonioKóczy, László T.2009-02-13T17:09:15Z20032003-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/pdfhttp://hdl.handle.net/10400.1/50engIFAC International Conference on Intelligent control Systems and Signal Processing (ICONS). - Faro, 8-11 Abril 2003. - 6 pAUT: ARU00698; CCA01443;info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:10:46Zoai:sapientia.ualg.pt:10400.1/50Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:54:29.447920Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Genetic programming and bacterial algorithm for neural networks and fuzzy systems design |
title |
Genetic programming and bacterial algorithm for neural networks and fuzzy systems design |
spellingShingle |
Genetic programming and bacterial algorithm for neural networks and fuzzy systems design Cabrita, Cristiano Lourenço Controlo automático Redes neuronais Sistemas fuzzy Programação genética Algoritmo bacteriano 681.5 Constructive algorithms B-splines Genetic programming Bacterial evolutionary algorithm Fuzzy rule base |
title_short |
Genetic programming and bacterial algorithm for neural networks and fuzzy systems design |
title_full |
Genetic programming and bacterial algorithm for neural networks and fuzzy systems design |
title_fullStr |
Genetic programming and bacterial algorithm for neural networks and fuzzy systems design |
title_full_unstemmed |
Genetic programming and bacterial algorithm for neural networks and fuzzy systems design |
title_sort |
Genetic programming and bacterial algorithm for neural networks and fuzzy systems design |
author |
Cabrita, Cristiano Lourenço |
author_facet |
Cabrita, Cristiano Lourenço Botzheim, J. Ruano, Antonio Kóczy, László T. |
author_role |
author |
author2 |
Botzheim, J. Ruano, Antonio Kóczy, László T. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Cabrita, Cristiano Lourenço Botzheim, J. Ruano, Antonio Kóczy, László T. |
dc.subject.por.fl_str_mv |
Controlo automático Redes neuronais Sistemas fuzzy Programação genética Algoritmo bacteriano 681.5 Constructive algorithms B-splines Genetic programming Bacterial evolutionary algorithm Fuzzy rule base |
topic |
Controlo automático Redes neuronais Sistemas fuzzy Programação genética Algoritmo bacteriano 681.5 Constructive algorithms B-splines Genetic programming Bacterial evolutionary algorithm Fuzzy rule base |
description |
In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use. |
publishDate |
2003 |
dc.date.none.fl_str_mv |
2003 2003-01-01T00:00:00Z 2009-02-13T17:09:15Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.1/50 |
url |
http://hdl.handle.net/10400.1/50 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IFAC International Conference on Intelligent control Systems and Signal Processing (ICONS). - Faro, 8-11 Abril 2003. - 6 p AUT: ARU00698; CCA01443; |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Faro |
publisher.none.fl_str_mv |
Faro |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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