Product quality prediction with support vector machines

  • Authors:

  • Xinggao Liu

  • Affiliations:

  • National Laboratory of Industrial Control Technology, Department of Control Science and Technology, Zhejiang University, Hangzhou, P.R. China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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Abstract

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.