Optimizing the performance of MLP and SVR predictors based on logical oring and experimental ranking equation

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dc.authoridhttp://orcid.org/0000-0001-8451-898Xen_US
dc.authoridhttp://orcid.org/0000-0002-6143-2734en_US
dc.contributor.authorAl-Rousan, Nadia
dc.contributor.authorAl-Najjar, Hazem
dc.date.accessioned2023-06-19T15:42:36Z
dc.date.available2023-06-19T15:42:36Z
dc.date.issued2021en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractImproving conventional prediction systems is widely used to optimize the learning process, achieve higher performance, and avoid overfitting. This paper’s purpose is to propose a new predictor for solar tracking systems applications based on oring operator and ranking equation with a conventional predictor including Multi-Layer Perceptron (MLP) and Support Vector Machine Regression (SVR). The point of using oring and ranking equation is to create a new variable that stores the information of combined attributes. This process aims to increase the accuracy of predictors and increase the efficiency of intelligent solar tracking systems. The experiments used 6 different datasets for solar tracking systems. The results revealed that the proposed predictors performed better than conventional predictors. Using the proposed predictors has improved both Root Mean Square Error (RMSE) and Coefficient of Determination (R2 ). The developed MLP models showed lower RMSE and higher R2 compared to conventional MLP models. The improvement ranges for using MLP are from 1.0013 to 1.4614 degrees for RMSE, and from 1.0019 to 1.4984 times for R2 , while the improvement ranges using SVM are from 1.001 to 1.988 degrees for RMSE and from 1.000 to 2.385 times for R2.en_US
dc.identifier.doi10.1080/02533839.2020.1856726en_US
dc.identifier.endpage157en_US
dc.identifier.issn0253-3839
dc.identifier.issn2158-7299
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85099388895en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage149en_US
dc.identifier.urihttps://hdl.handle.net/11363/4899
dc.identifier.urihttps://doi.org/
dc.identifier.volume44en_US
dc.identifier.wosWOS:000606937600001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAl-Rousan, Nadia
dc.institutionauthorAl-Najjar, Hazem
dc.language.isoenen_US
dc.publisherTAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLANDen_US
dc.relation.ispartofJournal of the Chinese Institute of Engineersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectOptimization modelen_US
dc.subjectmultilayer perceptronen_US
dc.subjectsupport vector machine regressionen_US
dc.subjectsingle axis trackeren_US
dc.subjectprediction modelen_US
dc.titleOptimizing the performance of MLP and SVR predictors based on logical oring and experimental ranking equationen_US
dc.typeArticleen_US

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