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基于随机森林回归分析的脉管制冷机性能预测模型
投稿时间:2021-05-10  修订日期:2021-05-16  点此下载全文
引用本文:赵鹏,陆志,蒋珍华,曲晓萍,吴亦农.基于随机森林回归分析的脉管制冷机性能预测模型[J].红外,2021,42(8):33~37
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作者单位E-mail
赵鹏 中国科学院上海技术物理研究所 hustinter@163.com 
陆志 中国科学院上海技术物理研究所  
蒋珍华 中国科学院上海技术物理研究所  
曲晓萍 中国科学院上海技术物理研究所  
吴亦农 中国科学院上海技术物理研究所 wyn@mail.sitp.ac.cn 
基金项目:国家自然科学基金项目(51806231)
中文摘要:为了探索星载脉管制冷机相关参数对制冷性能的影响和提高制冷性能的一致性,建立了基于机器学习的随机森林回归(Random Forest Regression, RFR)模型,然后对制冷性能与各个自变量进行了回归预测。制冷性能预测的平均相对误差为5.62%,平均确定性系数为0.805。按照特征重要度从高到低排序,前两位分别为丝网填充率和磁感应强度,与实际的实验结果相符(丝网填充率和磁感应强度的实际输入功的变化值分别为6.11 Wac和3.52 Wac,远大于其他4个自变量)。研究结果表明,RFR具有较高的精确度和鲁棒性,为提高星载脉管制冷机性能的一致性提供了新的思路。
中文关键词:脉管制冷机  随机森林回归  特征重要度
 
Cooling Performance Prediction Model of Pulse Tube Cryocooler Based on Random Forest Regression Analysis
Abstract:In order to explore the influence of relevant parameters on the cooling performance of space-borne pulse tube cryocooler and improve the consistency of cooling performance, a random forest regression model based on machine learning is established to make regression prediction of the cooling performance and various independent variables. The average relative error of cooling performance prediction is 5.62%, and the average certainty coefficient is 0.805. In terms of the influence degree of the variables, the first and second feature are mesh filling rate and magnetic induction intensity, which are consistent with the actual experimental results(the actual input power changes of mesh filling rate and magnetic induction intensity are 6.11 Wac and 3.52 Wac, which are much larger than the other four independent variables). The results show that RFR has the high accuracy and robustness, which provides a new idea for the consistency improvement of the cooling performance of space-borne pulse tube cryocooler.
keywords:pulse tube cryocooler  random forest regression  feature importance
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