Modeling Based on SOFM and the Dynamic ε-SVM for Fermentation Process
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.
KeywordsSupport Vector Machine Support Vector Machine Parameter Disjointed Region Standard Support Vector Machine Batch Data
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