Artikel Jurnal :: Kembali

Artikel Jurnal :: Kembali

Performance analysis of an automatic green pellet nuclear fuel quality classification using modified radial basis function neural networks

by Benyamin Kusumoputro, Dede Sutarya, Akhmad Faqih ([Publisher not identified] , 2016)

 Abstrak

Cylindrical uranium dioxide pellets, which are the main components for nuclear fuel elements in light water reactors, should have a high density profile, a uniform shape, and a minimum standard quality for their safe use as a reactor fuel component. The quality of green pellets is conventionally monitored by laboratory measurement of the physical pellet characteristics; however, this conventional classification method shows some drawbacks, such as difficult usage, low accuracy, and high time consumption. In addition, the method does not address the non-linearity and complexity of the relationship between pellet quality variables and pellet quality. This paper presents the development and application of a modified Radial Basis Function neural network (RBF NN) as an automatic classification system for green pellet quality. The weight initialization of the neural networks in this modified RBF NN is calculated through an orthogonal least squared method, and in conjunction with the use of a sigmoid activation function on its output neurons. Experimental data confirm that the developed modified RBF NN shows higher recognition capability when compared with that of the conventional RBF NNs. Further experimental results show that optimizing the quality classification problem space through eigen decomposition method provides a higher recognition rate with up to 98% accuracy.

 Metadata

Jenis Koleksi : Artikel Jurnal
No. Panggil : AJ-Pdf
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Subjek :
Penerbitan : [Place of publication not identified]: [Publisher not identified], 2016
Sumber Pengatalogan : LibUI eng rda
ISSN : 20872100
Majalah/Jurnal : International Journal of Technology (IJTECH)
Volume : Vol 7, No 4 (2016) 709-719
Tipe Konten : text
Tipe Media : computer
Tipe Carrier : online resource
Akses Elektronik : http://www.ijtech.eng.ui.ac.id/index.php/journal/article/view/3138
Institusi Pemilik : Universitas Indonesia
Lokasi :
  • Ketersediaan
  • Ulasan
  • Sampul
No. Panggil No. Barkod Ketersediaan
AJ-Pdf 03-20-898022738 TERSEDIA
Ulasan:
Tidak ada ulasan pada koleksi ini: 20449856
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