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Modeling Based on SOFM and the Dynamic ε-SVM for Fermentation Process

  • Xuejin Gao
  • Pu Wang
  • Chongzheng Sun
  • Jianqiang Yi
  • Yating Zhang
  • Huiqing Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

To overcome the deficiency of Support Vector Machine (SVM) for regression, dynamic ε-SVM method was proposed. To establish precise mathematical models, a new modeling method was introduced, combining self-organizing feature map (SOFM) with the dynamic ε-SVM. Firstly, SOFM was used as a clustering algorithm to partition the whole input space into several disjointed regions; then, the dynamic ε-SVM modeled for these partitioned regions. This method was illustrated by modeling penicillin fermentation process with plant field data. Results show that the method achieves significant improvement in generalization performance compared with other methods based on SVM.

Keywords

Support Vector Machine Support Vector Machine Parameter Disjointed Region Standard Support Vector Machine Batch Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xuejin Gao
    • 1
  • Pu Wang
    • 1
  • Chongzheng Sun
    • 1
  • Jianqiang Yi
    • 2
  • Yating Zhang
    • 1
  • Huiqing Zhang
    • 1
  1. 1.College of Electronic Information and Control EngineeringBeijing University of TechnologyBeijingChina
  2. 2.The Key Laboratory of Complex System and Intelligence ScienceChinese Academy of SciencesBeijingChina

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