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

Highlights: • Six principal features selection technique are utilized for parameters estimation. • Three major learning algorithms are combined with the selected FS techniques. • The MLP with the Bayesian regularization gives the more accurate results. • The proposed methodology can reduce the search space for more accurate estimation. 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.