• INFRARED
  • Vol. 42, Issue 8, 33 (2021)
Peng ZHAO1、2, Zhi LU1, Zhen-hua JIANG1, Xiao-ping QU1, and Yi-nong WU1、*
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3969/j.issn.1672-8785.2021.08.005 Cite this Article
    ZHAO Peng, LU Zhi, JIANG Zhen-hua, QU Xiao-ping, WU Yi-nong. Cooling Performance Prediction Model of Pulse Tube Cryocooler Based on Random Forest Regression Analysis[J]. INFRARED, 2021, 42(8): 33 Copy Citation Text show less
    References

    [1] Wilson K B, Fralick C C, Gedeon D R, et al. Sunpower′s CPT60 pulse tube cryocooler[J]. Cryocoolers, 2007, 14: 123-132.

    [2] Liu S S, Wu Y N, Zhang H, et al. Investigation on the inertance tube of pulse tube refrigerator operating at high temperature[J]. Energy, 2017, 123(3): 378-385.

    [3] Breiman L, Cutler A. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.

    [4] Breiman L. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140.

    [5] Ho T K. The random subspace method for constructing decision forests[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 832-844.

    ZHAO Peng, LU Zhi, JIANG Zhen-hua, QU Xiao-ping, WU Yi-nong. Cooling Performance Prediction Model of Pulse Tube Cryocooler Based on Random Forest Regression Analysis[J]. INFRARED, 2021, 42(8): 33
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