东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2005, Vol. 26 ›› Issue (11): 8-11.DOI: -

• 论著 • 上一篇    下一篇

基于支持向量机的生物发酵过程软测量建模

常玉清;王福利;王小刚;吕哲   

  1. 东北大学教育部暨辽宁省流程工业综合自动化重点实验室;东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院 辽宁沈阳110004;东北大学信息科学与工程学院;辽宁沈阳110004
  • 收稿日期:2013-06-24 修回日期:2013-06-24 出版日期:2005-11-15 发布日期:2013-06-24
  • 通讯作者: Chang, Y.-Q.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60374003);;

Soft sensing modeling based on support vector machines for fermentation process

Chang, Yu-Qing (1); Wang, Fu-Li (2); Wang, Xiao-Gang (2); Lu, Zhe (2)   

  1. (1) Key Laboratory of Process Industry Automation, Northeastern University, Shenyang 110004, China; (2) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-24 Revised:2013-06-24 Online:2005-11-15 Published:2013-06-24
  • Contact: Chang, Y.-Q.
  • About author:-
  • Supported by:
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摘要： 针对最小二乘向量机的缺陷,提出了一种改进的最小二乘支持向量机回归方法.根据输入变量和样本点间欧氏距离的大小,去除回归模型中大部分的样本点,从而获得回归模型的“稀疏”特性,大大提高计算速度.同时,将这一方法应用于生物发酵过程,建立了青霉素发酵过程中产物浓度的软测量模型,实现了青霉素浓度的在线预估.仿真结果表明,这一方法为生物发酵过程中难于在线测量质量参数的实时监测提供了一个有效的手段.

关键词: 软测量, 最小二乘支持向量机, 建模, 生物发酵, 青霉素浓度

Abstract: A regression method is proposed to improve the LS-SVM (least square-support vector machine) model. The sparseness of LS-SVM is thus obtained from the regression model to increase greatly the computation speed by way of removing most of the sample points in accordance to the Euclidian distances between input variables and sample points. The proposed method has been applied to the fermentation process to develop a soft sensing model so as to estimate the product's concentration on-line in penicillin fermentation process. Simulation results showed that the proposed method can provide a new useful approach to the real-time monitoring of quality variables which are hard to measure on-line in fermentation processes.

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