Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction

A deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent part is presented for renewable energy modeling and prediction. Beside the rule parameters, the values of horizontal slices and membership function (MF) parameters are also optimized. The stability of suggested...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yan Cao, Amir Raise, Ardashir Mohammadzadeh, Sakthivel Rathinasamy, Shahab S. Band, Amirhosein Mosavi
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Fuzzy logic Renewable energy Learning algorithm Deep learning Solar energy Wind turbines Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/9190d08ce3174b15803a94f466204d85
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9190d08ce3174b15803a94f466204d85
record_format	dspace
spelling	oai:doaj.org-article:9190d08ce3174b15803a94f466204d852021-11-28T04:33:51ZDeep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction2352-484710.1016/j.egyr.2021.07.004https://doaj.org/article/9190d08ce3174b15803a94f466204d852021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721004686https://doaj.org/toc/2352-4847A deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent part is presented for renewable energy modeling and prediction. Beside the rule parameters, the values of horizontal slices and membership function (MF) parameters are also optimized. The stability of suggested learning scheme is guaranteed. The proposed method is applied for modeling of both solar panels and wind turbines. By the use of experimental setup and generated real-world date sets, the applicability of suggested approach is shown. Comparison with convectional FLSs demonstrates the superiority of the suggested scheme.Yan CaoAmir RaiseArdashir MohammadzadehSakthivel RathinasamyShahab S. BandAmirhosein MosaviElsevierarticleFuzzy logicRenewable energyLearning algorithmDeep learningSolar energyWind turbinesElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 8115-8127 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fuzzy logic Renewable energy Learning algorithm Deep learning Solar energy Wind turbines Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Fuzzy logic Renewable energy Learning algorithm Deep learning Solar energy Wind turbines Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yan Cao Amir Raise Ardashir Mohammadzadeh Sakthivel Rathinasamy Shahab S. Band Amirhosein Mosavi Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction
description	A deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent part is presented for renewable energy modeling and prediction. Beside the rule parameters, the values of horizontal slices and membership function (MF) parameters are also optimized. The stability of suggested learning scheme is guaranteed. The proposed method is applied for modeling of both solar panels and wind turbines. By the use of experimental setup and generated real-world date sets, the applicability of suggested approach is shown. Comparison with convectional FLSs demonstrates the superiority of the suggested scheme.
format	article
author	Yan Cao Amir Raise Ardashir Mohammadzadeh Sakthivel Rathinasamy Shahab S. Band Amirhosein Mosavi
author_facet	Yan Cao Amir Raise Ardashir Mohammadzadeh Sakthivel Rathinasamy Shahab S. Band Amirhosein Mosavi
author_sort	Yan Cao
title	Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction
title_short	Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction
title_full	Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction
title_fullStr	Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction
title_full_unstemmed	Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction
title_sort	deep learned recurrent type-3 fuzzy system: application for renewable energy modeling/prediction
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/9190d08ce3174b15803a94f466204d85
work_keys_str_mv	AT yancao deeplearnedrecurrenttype3fuzzysystemapplicationforrenewableenergymodelingprediction AT amirraise deeplearnedrecurrenttype3fuzzysystemapplicationforrenewableenergymodelingprediction AT ardashirmohammadzadeh deeplearnedrecurrenttype3fuzzysystemapplicationforrenewableenergymodelingprediction AT sakthivelrathinasamy deeplearnedrecurrenttype3fuzzysystemapplicationforrenewableenergymodelingprediction AT shahabsband deeplearnedrecurrenttype3fuzzysystemapplicationforrenewableenergymodelingprediction AT amirhoseinmosavi deeplearnedrecurrenttype3fuzzysystemapplicationforrenewableenergymodelingprediction
_version_	1718408330714546176

Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction

Ejemplares similares