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Predicting the Superconducting Transition Temperature T c of BiPbSrCaCuOF Superconductors by Using Support Vector Regression

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Abstract

According to an experimental data set on the superconducting transition temperature (T c) of 21 BiPbSrCaCuOF superconductors under different process parameters including the amount of bismuth (n(Bi)), amount of oxygen (n(O)), sintering time (t) and sintering temperature (T), support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model for prediction of the T c of BiPbSrCaCuOF superconductors. The performance of SVR model was compared with that of back-propagation neural network (BPNN) and multivariable linear regression (MLR) model. The results show that the mean absolute error (MAE) and mean absolute percentage error (MAPE) of test samples achieved by SVR are smaller than those achieved by MLR or BPNN. This study suggests that SVR as a novel approach has a theoretical significance and potential practical value in development of high-T c superconductor via guiding experiment.

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Authors and Affiliations

  1. Department of Applied Physics, Chongqing University, Chongqing, 400044, P.R. China

    C. Z. Cai, X. J. Zhu, Y. F. Wen, J. F. Pei, G. L. Wang & W. P. Zhuang

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  1. C. Z. Cai
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  2. X. J. Zhu
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  3. Y. F. Wen
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  4. J. F. Pei
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  5. G. L. Wang
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  6. W. P. Zhuang
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Correspondence to C. Z. Cai.

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Cai, C.Z., Zhu, X.J., Wen, Y.F. et al. Predicting the Superconducting Transition Temperature T c of BiPbSrCaCuOF Superconductors by Using Support Vector Regression. J Supercond Nov Magn 23, 737–740 (2010). https://doi.org/10.1007/s10948-010-0732-x

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  • DOI: https://doi.org/10.1007/s10948-010-0732-x

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