期刊文献+

LS_ SVM和SVM在发酵过程建模中的比较 被引量:2

Comparison Studies of LS_ SVM and SVM on Modeling for Fermentation Process
下载PDF
导出
摘要 针对最小二乘支持向量机(LS_ SVM)不需要指定逼近精度ε的特点,比较了LS_ SVM与SVM两种方法利用生产数据为青霉素发酵过程建立的数学模型,改进型GA分别为LS_ SVM和SVM选择参数值.实验证明:LS_ SVM建立的模型具有较高的拟合精度和泛化能力.如果ε过大时,SVM建立的模型的拟合精度和泛化能力不高;当ε过小时,模型的拟合精度和泛化能力较高,但耗时多.因此,LS_SVM更适合为发酵过程建模. The SVM needs to use approximation accuracy ε,however the LS_ SVM doesn't need ε.According to this characteristics,the paper studied the fitting and generalization capabilities of models that LS_ SVM and SVM established for the penicillin fermentation process respectively.An improved GA selected the parameter values for LS_ SVM and SVM respectively.The experiment shows that the model based on LS-SVM possesses the strong capabilities of fitting and generalization.If ε is too large,the capabilities of fitting and generalization of model based on SVM are not high;if ε is too small,the capabilities of fitting and generalization are relatively high,but the modeling process demands long time.Therfore,the LS_ SVM is more suitable for modeling in fermentation processes.
出处 《北京工业大学学报》 EI CAS CSCD 北大核心 2010年第1期7-12,共6页 Journal of Beijing University of Technology
基金 国家自然科学基金资助项目(60704036) 北京工业大学青年基金资助项目(97002011200701) 北京工业大学博士科研启动基金资助项目(52002011200707)
关键词 支持向量机 最小二乘支持向量机 建模 青霉素发酵 遗传算法 support vector machine LS_ SVM modeling penicillin fermentation genetic algorithm
  • 相关文献

参考文献16

  • 1BECKER T, ENDERS T, DELGADO A. Dynamic neural networks as a tool for the online optimization of industrial fermentation[ J]. Bioprocess Biosyst Eng, 2002, 24(2) : 347-354.
  • 2NARENDRA K S, PARTHASARATHY K. Identification and control of dynamic systems using neural network [ J ]. IEEE Trans. on Neural Network, 1990, 1 (1) : 4-27.
  • 3VAPNIK V N. The nature of statistical learning theory[M]. New York: Springer Verlag, 1995: 123-180.
  • 4VAPNIK V N. An overview of statistical learning theory[J]. IEEE Trans. on Neural Network, 1999, 10(5) : 988-999.
  • 5GUO Guo-dong, LI S Z, CHAN K L. Support vector machines for face recognition[J]. Image and Vision Computing, 2001, 19(9) : 631-638.
  • 6CAO Li-juan. Support vector machines experts for time series forecasting[J]. Neurocomputing, 2003, 51 : 321-339.
  • 7SHEVADE S K, KEERTHI S S, BHATTACHARYYA C, et al. Improvements to the SMO algorithm for SVM regression[J]. IEEE Trans. on Neural Network, 2000, 11(5) : 1188-1193.
  • 8PAPADOPOULOS A, FOTIADIS D I, LIKAS A. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines [ J ]. Artificial Intelligence in Medicine, 2005, 34 (2) : 141-150.
  • 9SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[ J]. Neural Processing Letters, 1999, 9 (3) : 293-300.
  • 10YUAN Sheng-fa, CHU Fu-lei. Fault diagnostics based on particle swarm optimization and support vector machines [ J]. Mechanical Systems and Signal Processing, 2007, 21 (4) : 1787-1798.

同被引文献17

引证文献2

二级引证文献2

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