A neural network design for the estimation of nonlinear behavior of a magnetically-excited piezoelectric harvester

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dc.authoridKURT, EROL -- 0000-0002-3615-6926; Topaloglu, Nurettin -- 0000-0001-5836-7882
dc.contributor.authorÇelik, Emre
dc.contributor.authorUzun, Yunus
dc.contributor.authorKurt, Erol
dc.contributor.authorÖztürk, Nihat
dc.contributor.authorTopaloğlu, Nurettin
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T09:16:59Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T09:16:59Z
dc.date.issued2018
dc.departmentMühendislik Fakültesi
dc.description.abstractAn application of an artificial neural network (ANN) has been implemented in this article to model the nonlinear relationship of the harvested electrical power of a recently developed piezoelectric pendulum with respect to its resistive load R (L) and magnetic excitation frequency f. Prediction of harvested power for a wide range is a difficult task, because it increases dramatically when f gets closer to the natural frequency f (0) of the system. The neural model of the concerned system is designed upon the basis of a standard multi-layer network with a back propagation learning algorithm. Input data, termed input patterns, to present to the network and the respective output data, termed output patterns, describing desired network output that are carefully collected from the experiment under several conditions in order to train the developed network accurately. Results have indicated that the designed ANN is an effective means for predicting the harvested power of the piezoelectric harvester as functions of R (L) and f with a root mean square error of 6.65 x 10(-3) for training and 1.40 for different test conditions. Using the proposed approach, the harvested power can be estimated reasonably without tackling the difficulty of experimental studies and complexity of analytical formulas representing the concerned system.
dc.description.sponsorshipEuropean Union Ministry of Turkey, National Agency of Turkey [2015-1-TR01-KA203-021342]
dc.description.sponsorshipThe authors are grateful to the European Union Ministry of Turkey, National Agency of Turkey, for the support of this project under the Project Code 2015-1-TR01-KA203-021342 entitled Innovative European Studies on Renewable Energy Systems.
dc.identifier.doi10.1007/s11664-018-6078-z
dc.identifier.endpage4420en_US
dc.identifier.issn0361-5235
dc.identifier.issn1543-186X
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage4412en_US
dc.identifier.urihttps://doi.org/10.1007/s11664-018-6078-z
dc.identifier.urihttps://hdl.handle.net/20.500.12451/4683
dc.identifier.volume47en_US
dc.identifier.wosWOS:000437146400034
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Electronic Materials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Neural Network
dc.subjectPower
dc.subjectEstimation
dc.subjectPiezoelectric Harvester
dc.subjectExcitation Frequency
dc.titleA neural network design for the estimation of nonlinear behavior of a magnetically-excited piezoelectric harvester
dc.typeArticle

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