Product Quality Prediction with Support Vector Machines
Reliable prediction of melt index (MI) is crucial in practical propylene polymerization processes. In this paper, a least squares support vector machines (LS-SVM) soft-sensor model is developed first to infer the MI of polypropylene from other process variables. A weighted least squares support vector machines (weighted LS-SVM) approach is further proposed to obtain rather robust estimate. Detailed comparative researches are carried out among standard SVM, LS-SVM, and weighted LS-SVM. The research results confirm the effectiveness of the presented methods.
KeywordsSupport Vector Machine Root Mean Square Error Little Square Support Vector Machine Mean Relative Error Melt Index
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