Harmonic current classification using hybrid FAM-RBF neural network
In 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 (name...
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Institute of Advanced Engineering and Science
2023
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my.uniten.dspace-257752023-05-29T16:14:08Z Harmonic current classification using hybrid FAM-RBF neural network Leow S.Y. Yap K.S. Wong S.Y. 57193235970 24448864400 55812054100 In 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. Final 2023-05-29T08:14:07Z 2023-05-29T08:14:07Z 2020 Article 10.11591/ijeecs.v18.i3.pp1551-1558 2-s2.0-85079163757 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079163757&doi=10.11591%2fijeecs.v18.i3.pp1551-1558&partnerID=40&md5=c798704577935ed4f99b287ac9ee6dbc https://irepository.uniten.edu.my/handle/123456789/25775 18 3 1551 1558 All Open Access, Gold, Green Institute of Advanced Engineering and Science Scopus |
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In 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. |
| author2 |
57193235970 |
| author_facet |
57193235970 Leow S.Y. Yap K.S. Wong S.Y. |
| format |
Article |
| author |
Leow S.Y. Yap K.S. Wong S.Y. |
| spellingShingle |
Leow S.Y. Yap K.S. Wong S.Y. Harmonic current classification using hybrid FAM-RBF neural network |
| author_sort |
Leow S.Y. |
| title |
Harmonic current classification using hybrid FAM-RBF neural network |
| title_short |
Harmonic current classification using hybrid FAM-RBF neural network |
| title_full |
Harmonic current classification using hybrid FAM-RBF neural network |
| title_fullStr |
Harmonic current classification using hybrid FAM-RBF neural network |
| title_full_unstemmed |
Harmonic current classification using hybrid FAM-RBF neural network |
| title_sort |
harmonic current classification using hybrid fam-rbf neural network |
| publisher |
Institute of Advanced Engineering and Science |
| publishDate |
2023 |
| _version_ |
1806423271706460160 |
| score |
13.214268 |