期刊文献+

基于AdaBoost的飞机部件DMC预计方法研究

Study on DMC estimation of aircraft components based on AdaBoost
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摘要 飞机部件的直接维修成本(DMC)预计是控制整机维修成本的关键步骤。鉴于现有方法预计精度不高,波动性大,并针对经验数据匮乏的问题,引入了一种基于Ada Boost(adaptive boosting)算法的飞机部件直接维修成本预计方法。对比分析结果表明,此方法不依赖于经验数据,可较准确地预测飞机部件的实际直接维修成本,比以往的方法在精度和稳定性上有显著提高,适用于设计阶段及维修过程中的部件维修成本预计。 Component DMC estimation is a vital procedure in maintenance cost control. Aiming at the disadvantages of existing estimation methods such as low accuracy and large fluctuation, a new method based on AdaBoost (adaptive boosting) is introduced. Experiments show that this method does not rely on empirical data, it is obviously more accurate and stable than former ones. It is applicable to estimate the component DMC during design and maintenance process.
作者 徐建新 孙发东 XU Jianxin SUN Fadong(College of Aeronautical Engineering, CA UC, Tianjin 300300, Chin)
出处 《中国民航大学学报》 CAS 2016年第5期5-8,共4页 Journal of Civil Aviation University of China
关键词 飞机部件 直接维修成本 预计模型 偏最小二乘法 极端学习机 ADABOOST aircraft components DMC prediction model PLS ELM AdaBoost
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参考文献6

  • 1MEADOWS T A. Analysis of F/A-18 Engine Maintenance Costs Using the Boeing Dependability Cost Model[D]. Monterey: Naval Postgradu- ate School, 1994.
  • 2CUTLER R. Maintenance Engineering[R]. Toulouse: Airbus Industrie, 2003.
  • 3HUANG G B, ZHU Q Y, SIEW C K. Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks[C]//Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Bu- dapest, Hungary, 2004: 985-990.
  • 4HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1): 489-501.
  • 5钟诗胜,雷达.一种可用于航空发动机健康状态预测的动态集成极端学习机模型[J].航空动力学报,2014,29(9):2085-2090. 被引量:8
  • 6曹莹,苗启广,刘家辰,高琳.AdaBoost算法研究进展与展望[J].自动化学报,2013,39(6):745-758. 被引量:261

二级参考文献22

  • 1丁刚,徐敏强,侯立国.基于过程神经网络的航空发动机排气温度预测[J].航空动力学报,2009,24(5):1035-1039. 被引量:23
  • 2陈果,杨虞微.航空发动机复杂磨损趋势的神经网络多变量预测模型[J].中国机械工程,2007,18(1):70-74. 被引量:12
  • 3Schapire R E.The strength of weak learnability[J].Machine Learning,1990,5(2):197-227.
  • 4Freund Y,Schapire R E.A desicion-theoretic generalization of on-line learning and an application to boosting[J].Computational Learning Theory,1995,904:23-37.
  • 5Breiman L.Bagging predictors[J].Machine Learning,1996,24(2):123-140.
  • 6Solomatine D P,Shrestha D L.AdaBoost.RT:a boosting algorithm for regression problems[C]//Proceeding of 2004 IEEE International Joint Conference on Neural Networks.Budapest,Hungary:IEEE,2004:1163-1168.
  • 7Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70:489-501.
  • 8Huang G B,Wang D H,Lan Y.Extreme learning machines:a survey[J].International Journal of Machine Learning and Cybernetics,2011,2(2):107-122.
  • 9SHI Lichen,BAO Lianglu.EEG-based vigilance estimation using extreme learning machines[J].Neurocomputing,2013,102:135-143.
  • 10TIAN Huixin,MAO Zhizhong.An ensemble ELM based on modified AdaBoost.RT algorithm for predicting the temperature of molten steel in ladle furnace[J].IEEE Transactions on Automation Science and Engineering,2010,7(1):73-80.

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