期刊文献+

基于支持向量回归的放大器性能评价研究 被引量:5

Research on amplifier performance evaluation based on support vector regression machine
下载PDF
导出
摘要 本文提出了基于支持向量回归(SVR)的放大器性能评价方法。确定放大器性能评价系统结构,以带通滤波放大器为实验研究对象。首先通过幅频特性测试仪(型号E4403B、规格1Hz^3GHz)采样,得到带通滤波器幅频特性数据集,然后进行SVR回归,得到带通滤波放大器幅频特性曲线的逼近函数,用该函数对性能指标中的4项参数进行测定。实验表明,该方法提高了参数测量的精度,适于用示波法测量电子产品性能的评价。 An evaluation method for amplifier performance based on Support Vector Machine is presented. The amplifier performance evaluation system structure is determined and BPF amplifier is used as the experiment object. Firstly, the sample dataset of the amplitude-frequency characteristic for the BPF is obtained using amplitude frequency characteristic test equipment (Model: E4403B, Spec: 1 Hz -3 GHz), and then support vector regression is used to get the approach function of the amplitude-frequency characteristic of the BPF amplifier, and the obtained function is used to test the four performance parameters. Experimental results show that this method can improve the parameter test precision and is suitable for the evaluation of electronic product performance using oscilloscope.
出处 《仪器仪表学报》 EI CAS CSCD 北大核心 2008年第3期618-622,共5页 Chinese Journal of Scientific Instrument
关键词 支持向量回归 放大器性能评价 放大器幅频特性 support vector regression machine amplifier performance evaluation amplifier amplitude-frequency characteristic
  • 相关文献

参考文献8

  • 1CristianiniN Shawe-TaylorJ 李国正译.支持向量机导论[M].北京:电子工业出版社,2004..
  • 2BRAZDIL P, SOARES C, COSTA J. Ranking learning algorithms: Using IBL and meta-learning on accuracyand time results [ J ]. Machine Learning, 2003, 50 (3) : 251-277.
  • 3CHAPELLE O, VAPNIK V, BOUSQUET O, et al. Choosing multiple parameters for support vectormachines [ J ]. Machine Learning,2002,46 ( 1 ) : 131-159.
  • 4徐金标,刘丰,王育民.基于复数径向基函数神经网络的自适应均衡器[J].电子学报,1998,26(1):59-64. 被引量:2
  • 5SCHLKOPF B, BARTLETT P, SMOLA A, et al. Support vector regression with automatic accuracy control [C]. In: NIKLASSON L, BODEN, ZIEMKE T, eds. Proceedings of the ICANN' 98, Perspectives in Neural Computing. Berlin : Springer-Verlag, 1998 : 111-116.
  • 6SUYKENS J A K, LUKAS L, VAN DOOREN P, et al. Least squares support vector machine classifiers: a large scale algorithm [ C ]. In : European Conference on Circuit Theory and Design, ECCTD'99. 1999:839-842. http:// www. kernel-machines. org/papers/Su · ooMooVan99.ps. gz.
  • 7CRISTIANINI N, SHAWE-TAYLOR J, CAMPBELL C. Dynamically adapting kernels in support vector machines [ C]. In M. Kearns, S. Solla, &D. Cohn ( Eds. ) , Advances in Neural Information Processing Systems, 1998, 11: 204-210. MIT Press Available from http://www.kernel-machines. org.
  • 8BROWN M PS, GRUNDY W N, LIN D,et al. Knowledge-based analysis of microarray gene expression data by using support vector machines[ J]. In Proceedings of the National Academy of Sciences, 2000,97 (1) : 262-267.

二级参考文献5

  • 1Cha L,IEEE J SAC,1995年,13卷,1期,121页
  • 2Chen S,IEEE Trans Commun,1995年,43卷,5期,1937页
  • 3Chen S,EURASIP Signal Process,1994年,36卷,2期,175页
  • 4Chen S,EURASIP Signal Process,1994年,35卷,1期,19页
  • 5Chen S,EURASIP Signal Process,1991年,22卷,1期,77页

共引文献111

同被引文献45

引证文献5

二级引证文献15

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