弹道与气动力技术

基于支持向量机集成的导弹发动机压力预测

  • 刘丙杰 ,
  • 冀海燕 ,
  • 杨继锋
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  • 海军潜艇学院,山东青岛266071

刘丙杰(1979-),男,山西曲沃人,讲师,博士,研究方向:战略导弹可靠性工程与作战使用。

收稿日期: 2015-10-14

  网络出版日期: 2025-05-23

Prediction of Engine Pressure of Missile Based on Support Vector Machine Ensemble

  • LIU Bingjie ,
  • JI Haiyan ,
  • YANG Jifeng
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  • Navy Submarine Academy, Shandong Qingdao 266071, China

Received date: 2015-10-14

  Online published: 2025-05-23

摘要

针对导弹发动机压力下降难以预测的问题,根据某型导弹发动机压力实测数据,首先将实测数据进行插值处理,然后利用前5组数据为输入数据,后一组数据为预测数据,采用支持向量机集成方法对导弹发动机压力进行模型辨识,实现导弹发动机压力预测。通过对3组发动机压力实测数据进行仿真分析,发现支持向量机集成预测误差最大为0.102%,满足导弹发动机压力预测要求,对导弹发动机压力预防性维修具有重要作用。

本文引用格式

刘丙杰 , 冀海燕 , 杨继锋 . 基于支持向量机集成的导弹发动机压力预测[J]. 弹箭与制导学报, 2017 , 37(1) : 99 -102 . DOI: 10.15892/j.cnki.djzdxb.2017.01.023

Abstract

Since it was difficult to predict the pressure drop of missile engine, according to the measured data of engine pressure of a certain type of missile, the measured data were interpolated, then the five sets of data were used as input data, the latter set of data as forecast data, and the model identification of missile engine pressure was carried out by using support vector machine ensemble method to realise the prediction of missile engine pressure. Through the simulation analysis of three groups of engine pressure measured data, it was found that the maximum forcast error of support vector machine ensemble was 0.102%, which met the requirements of missile engine pressure prediction, and it played an important role in preventive maintenance of missile engine pressure.

参考文献

[1]
徐克俊. 航天发射故障诊断技术[M]. 北京: 国防工业出版社, 2007. 35.
[2]
何清, 李宁, 罗文娟,等. 大数据下的机器学习算法综述[J]. 模式识别与人工智能, 2014, 27 (4): 327- 336.
[3]
余凯, 贾磊, 陈雨强. 深度学习的昨天、今天和明天. 计算机研究与发展, 2013, 50 (9): 1799- 4804.
[4]
陈康, 向勇, 喻超. 大数据时代机器学习的新趋势[J]. 电信科学, 2012, 12 (4): 88- 95.
[5]
王晓丹, 高晓峰, 姚旭,等. SVM集成研究与应用[]. 空军工程大学学报, 2012, 12 (2): 84- 89.
[6]
刘爱华, 刘丙杰, 冀海燕,等. 基于自适应神经网络集成的电子设备故障预测方法[J]. 解放军理工大学学报, 2013, 14 (5): 565- 568.
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