|Table of Contents|

Prediction on thrust calibration slope based on FIG-SVR for attitude control rocket engine(PDF)

《火箭推进》[ISSN:1672-9374/CN:CN 61-1436/V]

Issue:
2015年03期
Page:
103-107
Research Field:
测控与试验
Publishing date:

Info

Title:
Prediction on thrust calibration slope based on FIG-SVR for attitude control rocket engine
Author(s):
CHEN Wen-li;  MA Jun-qiang;  YANG Si-feng;  TIAN Guo-hua
Beijing Institute of Aerospace Testing Technology, Beijing 100074, China
Keywords:
attitude control rocket engine test;  thrust calibration slope;  FIG-SVR;  prediction
PACS:
V434-34
DOI:
-
Abstract:
In order to predict the trend of thrust calibration slope for attitude control engine space simulation test, a method of time series prediction based on fuzzy information granulation (FIG) and support vector regression (SVR) is proposed in this paper. With the method of FIG, the thrust calibration slope is mapped as fuzzy information granulations (including minimum value Low, medium value R and maximum value Up) to reduce the dimension of the samples. The extracted parameters mentioned above are applied to SVR for proceeding regressive modeling. The prediction result shows that this method based on FIG-SVR can effectively predict the trend of the thrust calibration slope.

References:

[1]郭霄风. 液体火箭发动机试验[M]. 北京: 宇航出版社,1990.
[2]刘国球, 任汉芬, 朱宁昌， 等. 液体火箭发动机原理[M]. 北京： 中国宇航出版社, 2005.
[3]胡世祥. 运载火箭推进系统[M]. 北京: 国防工业出版社,2002.
[4]朱子环, 耿卫国, 陈锋. 液体火箭发动机试验推力校准控制系统的设计[J].计算机测量与控制, 2008, 16(11)： 1575-1577.

[5]张学工. 关于统计学习理论与支持向量机[J]. 自动化学报, 2000, 26(1): 32-41.

[6]TIPPING M E, FAUL A C. Fast marginal likelihood maximization for sparse Bayesian model[C]// Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics. Florida: [ s.n.], 2003: 120-127.
[7]田英杰. 支持向量回归及其应用研究[D]. 北京: 中国农业大学, 2005.
[8]和麟, 姜南, 黄潇瑶, 等. 基于模糊信息粒化SVM的飞机发电机故障预测[J]. 设计与研究, 2012(7): 7-10.
[9]张蕾. 基于小波和信息粒化BP神经网络的轴承故障诊断[J]. 机械科学与技术, 2012, 31(1): 49-52.
[10]ZADEH L A. Toward a theory of fuzzy information gra- nulation and its centrality in human reasoning and fuzzy logic [J]. Fuzzy Sets and Systems, 1997, 90(2): 111-127.
[11]李洋．基于信息粒化的机器学习分类及回归预测分析[D]. 北京: 北京师范大学数学科学学院, 2009.
[12]林左鸣, 谭瑞松. 航空故障诊断与健康管理技术[M]. 北京: 航空工业出版社, 2013.
[13]彭勇, 陈俞强. 基于信息粒化得SVM时序回归预测[J]. 计算机系统应用, 2013, 22(5): 163-167.

Memo

Memo:
-
Last Update: 1900-01-01