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)


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.


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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  1. 1.
    Ning, L., Shaoyuan, L., Yugeng, X.: A Multiple Model Approach to Modeling Based on LPF Algorithm. J of System Engineering and Electronics 12(3), 64–70 (2001)Google Scholar
  2. 2.
    Foss, B.A., Johansen, T.A., Sorensen, A.V.: Nonlinear Predictive Control using Local Models Applied to a Batch Fermentation Process. Control Engineering Practice 3(3), 389–396 (1995)CrossRefGoogle Scholar
  3. 3.
    Mingyong, Q.: Optimization Control of Fermentation Engineering. Jiangsu Science and Technology Publishing House, Nanjing (1998)Google Scholar
  4. 4.
    Shuqing, W., Yingjin, Y.: Automation Technology of Biochemistry Process. Chemical Industry Press, Beijing (1999)Google Scholar
  5. 5.
    Simon, L., Karim, M.N., Schreiweis, A.: Prediction and Classification of Different Phases in a Fermentation using Neural Networks. Biotechnology Techniques 12(4), 301–304 (1998)CrossRefGoogle Scholar
  6. 6.
    Lijuan, C.: Support Vector Machines Experts for Time Series Forecasting. Neurocomputing 51, 321–339 (2003)CrossRefGoogle Scholar
  7. 7.
    Kohonen, T.: The Self-organizing Map. Springer, Heidelberg (1995)Google Scholar
  8. 8.
    Becker, T., Enders, T., Delgado, A.: Dynamic Neural Networks as A Tool for the Online Optimization of Industrial Fermentation. Bioprocess Biosyst. Eng. 24(2), 347–354 (2002)Google Scholar
  9. 9.
    Zuo, K., Wu, W.T.: Semi-realtime Optimization and Control of A Fed-batch Fermentation System. Computers and Chemical Eng. 24(2), 1105–1109 (2000)CrossRefGoogle Scholar
  10. 10.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)MATHGoogle Scholar
  11. 11.
    Vapnik, V.N.: An Overview of Statistical Learning Theory. IEEE Trans. on Neural Network 10(5), 988–999 (1999)CrossRefGoogle Scholar
  12. 12.
    Guodong, G., Li, S., Luk, C.K.: Support Vector Machines for Face Recognition. Image and Vision Computing 19(9), 631–638 (2001)CrossRefGoogle Scholar
  13. 13.
    Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., et al.: Improvements to the SMO Algorithm for SVM Regression. IEEE Trans. on Neural Network 11(5), 1188–1193 (2000)CrossRefGoogle Scholar
  14. 14.
    Yuhong, W., Dexian, H., Dongjie, G., et al.: Nonlinear Predictive Control Based on LS-SVM. Control and Decision 19(4), 383–387 (2004)MATHGoogle Scholar
  15. 15.
    Qinggui, Z.: Introduction of Artificial Neural Networks. China WaterPower Press, Beijing (2004)Google Scholar

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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