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Investigating the performance of the supervised learning algorithms for estimating NPPs parameters in combination with the different feature selection techniques

Moshkbar Bakhshayesh, K ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1016/j.anucene.2021.108299
  3. Publisher: Elsevier Ltd , 2021
  4. Abstract:
  5. Several reasons such as no free lunch theorem indicates that any learning algorithm in combination with a specific feature selection (FS) technique may give more accurate estimation than other learning algorithms. Therefore, there is not a universal approach that outperforms other algorithms. Moreover, due to the large number of FS techniques, some recommended solutions such as using synthetic dataset or combining different FS techniques are very tedious and time consuming. In this study to tackle the issue of more accurate estimation of NPPs parameters, the performance of the major supervised learning algorithms in combination with the different FS techniques which are appropriate for parameters estimation is considered. The target parameters/transients of the Bushehr nuclear power plant (BNPP) are examined as the case study. By comparing three major supervised learning algorithms (i.e. the MLP-BR, the MLP-LM, and the SVM) in combination with six principal FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) for estimation of three important parameters of NPP (i.e. FMT, CMT, and the DNBR), the BR learning algorithm gives the more accurate results. Therefore, the results show that if the number of FS techniques is m and the number of learning algorithms is n, the search space for more accurate estimation of the NPPs important parameters can be reduced from n × m to 1 × m. © 2021 Elsevier Ltd
  6. Keywords:
  7. Large dataset ; Learning algorithms ; Nuclear fuels ; Nuclear power plants ; Parameter estimation ; Support vector machines ; Accurate estimation ; Bayesian regularization ; Feature selection technique ; Features selection ; No free lunch theorem ; NPP parameter estimation ; Performance ; Supervised learning algorithm ; Universal approach ; Feature extraction
  8. Source: Annals of Nuclear Energy ; Volume 158 , 2021 ; 03064549 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0306454921001754