期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

Thrust estimator design based on least squares support vector regression machine

Thrust estimator design based on least squares support vector regression machine
下载PDF
导出
摘要 In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem. Furthermore,numerical simulations confirm the effectiveness of our presented scheme. During the process of estimator design,a wrapper criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator. In order to realize direct thrust control instead of traditional sensor-based control for aero-engines, it is indispensable to design a thrust estimator with high accuracy, so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem. Furthermore, numerical simulations confirm the effectiveness of our presented scheme. During the process of estimator design, a wrapper criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator.
作者 赵永平 孙健国
机构地区 Intelligent Ammunition Tecnology Laboratory of National Defence College of Energy and Power Engineering
出处 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2010年第4期578-583,共6页 哈尔滨工业大学学报（英文版）
基金 Sponsored by the National Natural Science Foundation of China ( Grant No 50576033)
关键词 least squares support vector machine direct thrust control wrapper criterion least squares support vector machine direct thrust control wrapper criterion
分类号 V231 [航空宇航科学与技术—航空宇航推进理论与工程]
  • 相关文献

参考文献2

二级参考文献22

  • 1陈恬,孙健国.基于相关性分析和神经网络的直接推力控制[J].南京航空航天大学学报,2005,37(2):183-187. 被引量：9
  • 2Zeng X Y, Chen X W. SMO-based pruning methods for sparse least squares support vector machines[J]. IEEE Trans on Neural Networks, 2005, 16(6):1541-1546.
  • 3Jiao L C, Bo L F, Wang L. Fast sparse approximation for least squares support vector machine[J]. IEEE Trans on Neural Networks, 2007, 18(3): 685-697.
  • 4Zhao Y, Keong K C. Fast leave-one-out evaluation and improvement on inference for LS-SVMs[C]. Proc of the 17th Int Conf on Pattern Recognition. Cambridge: IEEE, 2004: 494-497.
  • 5An S J, Liu W Q, Venkatesh S. Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression[J]. Pattern Recognition, 2007, 40(8):2154-2162.
  • 6Murphu P M, Aha D W. UCI repository of machine learning database [Z]. http://www.ics. uei. edu/-mlearn/MLRepository.html.
  • 7Vapnik V N. The nature of statistical learning theory[M]. New York: Springer-Verlag, 1995.
  • 8Meng Z M, Peng L F, Zhou G G, et al. A multiclassification method of temporal data based on support vector machine[C]. Proc of LNAI. Berlin:Springer-Verlag, 2007: 240-249.
  • 9Huang C L, Chen M C, Wang C J. Credit scoring with a data mining approach based on support vector machines[J]. Expert Systems with Application, 2007,33(4): 847-856.
  • 10Suykens J A K, Vandewalle J. Least squares support vector machine classifiers [J]. Neural Processing Letters, 1999, 9(3):293-300.

共引文献22

Journal of Harbin Institute of Technology(New Series)
2010年 第4期

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