Long Term Prediction of Product Quality in a Glass Manufacturing Process Using a Kernel Based Approach

  • Tobias Jung
  • Luis Herrera
  • Bernhard Schoelkopf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3512)


In this paper we report the results obtained using a kernel-based approach to predict the temporal development of four response signals in the process control of a glass melting tank with 16 input parameters. The data set is a revised version from the modelling challenge in EUNITE-2003. The central difficulties are: large time-delays between changes in the inputs and the outputs, large number of data, and a general lack of knowledge about the relevant variables that intervene in the process. The methodology proposed here comprises Support Vector Machines (SVM) and Regularization Networks (RN). We use the idea of sparse approximation both as a means of regularization and as a means of reducing the computational complexity. Furthermore, we will use an incremental approach to add new training examples to the kernel-based method and efficiently update the current solution. This allows us to use a sophisticated learning scheme, where we iterate between prediction and training, with good computational efficiency and satisfactory results.


Support Vector Machine Support Vector Regression Reproduce Kernel Hilbert Space Gaussian Process Regression Sparse Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tobias Jung
    • 1
  • Luis Herrera
    • 2
  • Bernhard Schoelkopf
    • 3
  1. 1.Fachbereich Mathematik & InformatikJohannes Gutenberg-UniversitaetMainzGermany
  2. 2.Dpt. of Computer Architecture and TechnologyUniversity of GranadaGranadaSpain
  3. 3.M.P.I. for Biological CyberneticsTuebingenGermany

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