Support Vector Machines and Genetic Algorithms for Soft-Sensing Modeling

  • Authors:

  • Sang Haifeng;Yuan Weiqi;Wang Fuli;He Dakuo

  • Affiliations:

  • School of Information Science and Engineering, Shenyang University of Technology 663#, Shenyang, 110023, China;School of Information Science and Engineering, Shenyang University of Technology 663#, Shenyang, 110023, China;School of Information Science and Engineering, Northeastern University 131#, Shenyang, 110004, China;School of Information Science and Engineering, Northeastern University 131#, Shenyang, 110004, China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
  • Year:
  • 2007

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Abstract

Soft sensors have been widely used in industrial process control to improve the quality of product and assure safety in production. This paper introduces support vector machines (SVM) into soft-sensing modeling. Building the models, on one hand we want to have the best set of input variables, on the other hand we want to get the best possible performance of the SVM model. So the Genetic Algorithms is used to choose the input variables and select the parameters of SVM. Moreover, training the model on data coming a real experiment process--Nosiheptidefermentation process and evaluating the model performance on the same process. Results show that SVM model optimized by Genetic Algorithms provides a new and effective method for soft- sensing modeling and has promising application in industrial process applications.