Quality classification of green pellet Nuclear fuels using Radial Basis Function neural networks

Benyamin Kusumo Putro, Akhmad Faqih, Dede Sutarya, Lina

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)

Abstract

Total quality classification process is necessary to be continously conducted along the pellet fabrication processes to minimize the number of rejected of the green pellets. This cylindrical uranium dioxide pellets, as the main fuel element in the Light Water Nuclear Reactor, should shows uniform shape, uniform quality and a high density profile. The quality of green pellets is conventionally monitored through a laboratory measurement of the physical pellets characteristics followed by a graphical chart classification technique, however, this technique is difficult to use and shows low accuracy and time consuming. In this paper, a Radial Basis Function neural networks is develop by studied and modified the weight initialization on its neural structure, and applied for automation of classifying the pellets quality. It is proved from the experiments that the Radial Basis Function neural networks shows a comparable classification rate with that of best-tune Back Propagation neural networks, however, the computational cost is reduced significantly.

Original languageEnglish
Pages194-198
Number of pages5
DOIs
Publication statusPublished - 1 Jan 2013
Event2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 - Miami, FL, United States
Duration: 4 Dec 20137 Dec 2013

Conference

Conference2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
CountryUnited States
CityMiami, FL
Period4/12/137/12/13

Keywords

  • classification of green pellets
  • nuclear fuel cells
  • RBF NN
  • Weight initialization

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