A Study of Soft Computing Techniques in Chemical Reactions

Detalhes bibliográficos
Autor(a) principal: RSY, Dr. Ramjeet Singh Yadav
Data de Publicação: 2021
Tipo de documento: Artigo
Idioma: eng
Título da fonte: INFOCOMP: Jornal de Ciência da Computação
Texto Completo: https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1218
Resumo: This research paper has been described the fuzzy logic and hybrid fuzzy logic-based systems, which is utilize to create knowledge-based frameworks in chemical engineering.  In this proposed study, we have proposed fuzzy logic-based methods such as Fuzzy sets, Fuzzy C-Means (FCM), Subtractive Clustering (SC) and integrated approach of SC and Artificial Neural Network Fuzzy Inference System (SC-ANFIS) for calculating the rate of chemical reaction. After comparing the results of these proposed methods with other exiting methods such as classical fuzzy logic, FCM and subtractive clustering (SC) methods, gives get the better result of SC-ANFIS. The root mean square error (both training and testing data) of SC-ANIFIS is less as compared to existing FCM method.  
id UFLA-5_47baad2270891fb201ce2468733c5b36
oai_identifier_str oai:infocomp.dcc.ufla.br:article/1218
network_acronym_str UFLA-5
network_name_str INFOCOMP: Jornal de Ciência da Computação
repository_id_str
spelling A Study of Soft Computing Techniques in Chemical ReactionsThis research paper has been described the fuzzy logic and hybrid fuzzy logic-based systems, which is utilize to create knowledge-based frameworks in chemical engineering.  In this proposed study, we have proposed fuzzy logic-based methods such as Fuzzy sets, Fuzzy C-Means (FCM), Subtractive Clustering (SC) and integrated approach of SC and Artificial Neural Network Fuzzy Inference System (SC-ANFIS) for calculating the rate of chemical reaction. After comparing the results of these proposed methods with other exiting methods such as classical fuzzy logic, FCM and subtractive clustering (SC) methods, gives get the better result of SC-ANFIS. The root mean square error (both training and testing data) of SC-ANIFIS is less as compared to existing FCM method.  Editora da UFLA2021-06-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1218INFOCOMP Journal of Computer Science; Vol. 20 No. 1 (2021): June 20211982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1218/566Copyright (c) 2021 Dr. Ramjeet Singh Yadav RSYinfo:eu-repo/semantics/openAccessRSY, Dr. Ramjeet Singh Yadav2021-06-04T11:27:56Zoai:infocomp.dcc.ufla.br:article/1218Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:46.469178INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv A Study of Soft Computing Techniques in Chemical Reactions
title A Study of Soft Computing Techniques in Chemical Reactions
spellingShingle A Study of Soft Computing Techniques in Chemical Reactions
RSY, Dr. Ramjeet Singh Yadav
title_short A Study of Soft Computing Techniques in Chemical Reactions
title_full A Study of Soft Computing Techniques in Chemical Reactions
title_fullStr A Study of Soft Computing Techniques in Chemical Reactions
title_full_unstemmed A Study of Soft Computing Techniques in Chemical Reactions
title_sort A Study of Soft Computing Techniques in Chemical Reactions
author RSY, Dr. Ramjeet Singh Yadav
author_facet RSY, Dr. Ramjeet Singh Yadav
author_role author
dc.contributor.author.fl_str_mv RSY, Dr. Ramjeet Singh Yadav
description This research paper has been described the fuzzy logic and hybrid fuzzy logic-based systems, which is utilize to create knowledge-based frameworks in chemical engineering.  In this proposed study, we have proposed fuzzy logic-based methods such as Fuzzy sets, Fuzzy C-Means (FCM), Subtractive Clustering (SC) and integrated approach of SC and Artificial Neural Network Fuzzy Inference System (SC-ANFIS) for calculating the rate of chemical reaction. After comparing the results of these proposed methods with other exiting methods such as classical fuzzy logic, FCM and subtractive clustering (SC) methods, gives get the better result of SC-ANFIS. The root mean square error (both training and testing data) of SC-ANIFIS is less as compared to existing FCM method.  
publishDate 2021
dc.date.none.fl_str_mv 2021-06-13
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1218
url https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1218
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/1218/566
dc.rights.driver.fl_str_mv Copyright (c) 2021 Dr. Ramjeet Singh Yadav RSY
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Dr. Ramjeet Singh Yadav RSY
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editora da UFLA
publisher.none.fl_str_mv Editora da UFLA
dc.source.none.fl_str_mv INFOCOMP Journal of Computer Science; Vol. 20 No. 1 (2021): June 2021
1982-3363
1807-4545
reponame:INFOCOMP: Jornal de Ciência da Computação
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str INFOCOMP: Jornal de Ciência da Computação
collection INFOCOMP: Jornal de Ciência da Computação
repository.name.fl_str_mv INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv infocomp@dcc.ufla.br||apfreire@dcc.ufla.br
_version_ 1799874742653550592