期刊文献+

基于LS-SVM的虚拟FDR及其在操纵面偏角信号重构中的应用

Virtual FDR Based on LS-SVM and Its Applications to Reconstruction of Control Surface Angular Deflection Signals
下载PDF
导出
摘要 为重构操纵面偏角信号,提出了基于最小二乘支持向量机(LS-SVM)的虚拟飞行数据记录器设计.在设计中,LS-SVM被作为仿真器和重构器.仿真器用来模拟飞机的飞行动态特性,重构器用于来重构主操纵面偏转信号.仿真结果表明:本设计是重构操纵面偏转参数的强有力工具,在飞行事故调查中有非常广阔的应用前景. For the reconstruction of Control Surfaces Angular Deflection (CSAD) signals, a scheme of Virtual Flight Data Recorder (VFDR), which is based on Least Squares Support Vector Machines (LS-SVM), is proposed. The scheme consists of LS-SVM Simulator (LS-SVMS) and LS-SVM Reconstructor (LS-SVMR). LS- SVMS is used to simulate the flight dynamics of aircraft, and LS-SVMR to reconstruct the CSAD. The simulation shows that the scheme provides a powerful tool for reconstruction of CSAD and has a promising application to the investigation of aircraft crashes.
出处 《昆明理工大学学报(理工版)》 北大核心 2010年第2期61-65,共5页 Journal of Kunming University of Science and Technology(Natural Science Edition)
关键词 最小二乘 支持向量机 虚拟飞行数据记录器 飞行事故调查 least square SVM virtual FDR investigation of aircraft crash
  • 相关文献

参考文献9

  • 1Napolitano M R,Martinelli D R,Windon D A,et al.virtual Flight Data Recorder:A Neural Extension of Existing Flight Data Recorder Capabilities[J].AIAA Guidance,Navigation,and Control Conference,1997.
  • 2Napolitano M R,Windon D A.Neural Networks-Based Reconstruction of Flight Data for Aircraft Crash Investigation[A].In:Proceedings of the International Conference on Neural Networks,Washington,DC,1996.
  • 3Napolitano M R,Casanova J L,Windon D A,et al.Neural and Fuzzy Reconstructors for the Virtual Flight Data Recorder[J].IEEE Transactions on Aerospace and Electronic Systems,1999,35(1):61-70.
  • 4Vapnik V.Statistical Learning Theory[M].New York:Wiley,1998.
  • 5Suykens J A K,Gestel V T,Brabanter J De,et al.Least Squares Support Vector Machines[M].World Scientific,2002.
  • 6Jiao L C,Bo L F,Wang L.Fast Sparse Approximation for Least Squares Support Vector Machine[J].IEEE Trans-actions on Neural Networks,277,18(3):685-697.
  • 7An S J,Liu W Q,Venkatesh S M.Fast cross-validation aorithms for least squares support vector machine and kernel ridge regression[J].Pattern Recognition,2007,40(8):2154-2162.
  • 8Espinoza M,Suykens J A K,De Moor B.Load forecasting using fixed-size least squares support vector machines[A].8th International Workshop on Artificial Neural Networks,IWANN 2005:Computational Intelligence and Bioinspired System,Vilan-ovaila Geltru:Springer-Verlag,2005.
  • 9Hung Y H,Liao Y S.Applying PCA and Fixed Size LS-SVM Method for Large Scale Classification Problem[J].Information Technology Journal,2008,7(6):890-896.

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部