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

作者: Khalil Moshkbar-Bakhshayesh

DOI: 10.1016/J.ANUCENE.2021.108299

关键词:

摘要: … The important types of static approach for calculation of variables correlation are the Kendall’s tau, the Pearson, and the Spearman which are given by Eq. (10), Eq. (11), and Eq. …

参考文章(23)
Yong-kuo Liu, Fei Xie, Chun-li Xie, Min-jun Peng, Guo-hua Wu, Hong Xia, Prediction of time series of NPP operating parameters using dynamic model based on BP neural network Annals of Nuclear Energy. ,vol. 85, pp. 566- 575 ,(2015) , 10.1016/J.ANUCENE.2015.06.009
Marko Robnik-Šikonja, Igor Kononenko, Theoretical and Empirical Analysis of ReliefF and RReliefF Machine Learning. ,vol. 53, pp. 23- 69 ,(2003) , 10.1023/A:1025667309714
Bing Xue, Mengjie Zhang, Will N. Browne, Particle swarm optimisation for feature selection in classification soft computing. ,vol. 18, pp. 261- 276 ,(2014) , 10.1016/J.ASOC.2013.09.018
Robert E. Uhrig, J.Wesley Hines, COMPUTATIONAL INTELLIGENCE IN NUCLEAR ENGINEERING Nuclear Engineering and Technology. ,vol. 37, pp. 127- 138 ,(2005)
Khalil Moshkbar-Bakhshayesh, Mohammad B. Ghofrani, Transient identification in nuclear power plants: A review Progress in Nuclear Energy. ,vol. 67, pp. 23- 32 ,(2013) , 10.1016/J.PNUCENE.2013.03.017
Robert E. Uhrig, Lefteri H. Tsoukalas, Soft computing technologies in nuclear engineering applications Progress in Nuclear Energy. ,vol. 34, pp. 13- 75 ,(1999) , 10.1016/S0149-1970(97)00109-1
E. Zio, P. Baraldi, D. Roverso, An extended classifiability index for feature selection in nuclear transients Annals of Nuclear Energy. ,vol. 32, pp. 1632- 1649 ,(2005) , 10.1016/J.ANUCENE.2005.06.003
Othman Soufan, Dimitrios Kleftogiannis, Panos Kalnis, Vladimir B. Bajic, DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm PLOS ONE. ,vol. 10, pp. e0117988- ,(2015) , 10.1371/JOURNAL.PONE.0117988