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Title Performance analysis of an automatic green pellet nuclear fuel quality classification using modified radial basis function neural networks / Benyamin Kusumoputro, Dede Sutarya, Akhmad Faqih
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ISBN/ISSNnone20872100
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VolumeVol 7, No 4 (2016) 709-719
Electronic Access http://www.ijtech.eng.ui.ac.id/index.php/journal/article/view/3138
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PDF 03-17-355520186 TERSEDIA
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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.
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