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.
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Gao, X., Wang, P., Sun, C., Yi, J., Zhang, Y., Zhang, H. (2006). Modeling Based on SOFM and the Dynamic ε-SVM for Fermentation Process. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_19
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DOI: https://doi.org/10.1007/11816157_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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