期刊文献+

一种电子设备电磁脉冲响应预测新方法 被引量:2

A new method of predicting the electromagnetic pulse response of the electron device
下载PDF
导出
摘要 针对现有机理建模算法普遍存在计算电磁脉冲过于复杂的问题,探索基于实验统计的电磁脉冲响应预测新方法,使用开关电源进行雷击浪涌注入实验,并记录不同注入电压下的输入输出数据。依据系统辨识理论选用ARX模型对开关电源进行建模,并利用最小二乘算法对模型参数进行辨识。通过所建模型预测不同幅值浪涌激励的响应并与实验结果对比,发现该模型能准确预测脉冲响应,验证了系统辨识建模的有效性,并且计算过程得到简化,为电磁防护工程应用提供新的参考方法。 The ubiquity problems that the process computing electromagnetic pulse response by the existing mechanism modeling algorithms is too complex.In order to study the new methods of predicting electromagnetic pulse response based on experiment data,the switch voltage power is used to carry on the lightning surge experiment,recording the input and output data of different amplitude lightning voltage.The model of the switch voltage power is built by ARX model based on least square algorithm estimating its parameters.Comparing the predicting response of different amplitude lightning surge voltages with the experiment results,it discovers that the model can predict the response pulse correctly and approve the effective of system identification modeling,simplify computing process,and it offers a reference method for electromagnetic protection engineering application.
出处 《国外电子测量技术》 2011年第9期36-39,共4页 Foreign Electronic Measurement Technology
关键词 电磁脉冲 雷击浪涌 系统辨识 ARX模型 最小二乘算法 electromagnetic pulse lightning surge system identification ARX model least square algorithm
  • 相关文献

参考文献9

二级参考文献84

共引文献123

同被引文献26

  • 1宋海滨,刘云帼.基于支持向量机的预测控制算法[J].兵工自动化,2006,25(4):59-61. 被引量:2
  • 2邹涛,王昕,李少远.基于混合逻辑的非线性系统多模型预测控制[J].自动化学报,2007,33(2):188-192. 被引量:18
  • 3张弦,李世平,孙浚清,唐超.基于灰色神经网络组合模型的动态数据序列预测[J].电子测量技术,2007,30(9):60-63. 被引量:10
  • 4HUG H, MAO ZH ZH, HE D K, et al. Hybrid model- ing for the prediction of leaching rate in leaching process based on negative correlation learning bagging ensemble algorithm [ J ]. Computers Chemical Engineering, 2011, 35(12) : 2611- 2617.
  • 5DE ANDRADE LIMA L R P, HODOUIM D. Multivariate statistical analysis of gold cyanidation plant data[ J ]. Int. J. Mining and Mineral Engineering, 2008, 1 ( 1 ) : 113-126.
  • 6JUN C H, LEE S H, PARK H S, et ol. Use of partial least squares regression for variable selection and quality prediction[ C ]. International Conference on Computers & Industrial Engineering, Troyes, 2009 : 1302-1307.
  • 7SALEH M A, SABAH A A, CHARLES S B. Combining principal component regression and artificial neural net- works for more accurate predictions of ground level ozone [ J ]. Environmental Modeling & Software, 2008, 23 (4) : 396-403.
  • 8INGE K, KANTA N. Prediction of muhivariate responses with a selected number of principal components [ J ]. Computational Statistics & Data Analysis, 2010, 54(7 ) : 1791-1907.
  • 9HAN H G, CHEN Q L, QIAO J F. An efficient self-or- ganizing RBF neural network for water quality prediction [J]. Neural Networks, 2011, 24(7) : 717-725.
  • 10陈刚,路殿坤.湿法冶金原理与工艺[M].沈阳:东北大学出版社,1999.

引证文献2

二级引证文献8

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