Modeling based on SOFM and the dynamic ε-SVM for fermentation process

Authors:
Xuejin Gao;Pu Wang;Chongzheng Sun;Jianqiang Yi;Yating Zhang;Huiqing Zhang
Affiliations:
College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China;College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China;College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China;The Key Laboratory of Complex System and Intelligence Science, Chinese Academy of Sciences, Beijing, China;College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China;College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

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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.