Please use this identifier to cite or link to this item: http://dspace2020.uniten.edu.my:8080/handle/123456789/20749
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dc.contributor.authorLeow S.Y.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorWong S.Y.en_US
dc.date.accessioned2021-07-29T08:25:40Z-
dc.date.available2021-07-29T08:25:40Z-
dc.date.issued2020-
dc.identifier.urihttp://dspace2020.uniten.edu.my:8080/handle/123456789/20749-
dc.description.abstractIn this paper, the type of customers of electricity in Malaysia is classified into the type of electricity consumers based on the harmonic current data. A hybrid of Fuzzy Adaptive Resonance Theory with Mapping Algorithm (Fuzzy ARTMAP) and Radial Basis Function (RBF) neural network is developed (namely FAM-RBF), and it is used to classify the harmonic current into types of consumers. The result of the proposed neural network is discussed, and compared with other neural networks in this paper. The comparison result shows that the proposed FAM-RBF obtained the best performance result and is a truthful neural network to be used in this application. Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.language.isoenen_US
dc.titleHarmonic current classification using hybrid FAM-RBF neural networken_US
dc.typearticleen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextreserved-
item.cerifentitytypePublications-
item.openairetypearticle-
item.fulltextWith Fulltext-
item.languageiso639-1en-
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