Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

Neural Computing and Applications - Tập 33 Số 24 - Trang 17131-17145 - 2021
P.J. Garcı́a Nieto1, Esperanza García–Gonzalo1, José P. Paredes–Sánchez2
1Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain
2Department of Energy, College of Mining, Energy and Materials Engineering, University of Oviedo, 33004 Oviedo, Spain

Tóm tắt

AbstractThis study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.

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Neural Computing and Applications

Tập 33 Số 24

17131-17145

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