Header logo is ei

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

2005

Conference Paper

ei


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 version1 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.

Author(s): Jung, T. and Herrera, L. and Schölkopf, B.
Book Title: Proceedings of the 8th International Work-Conferenceon Artificial Neural Networks (Computational Intelligence and Bioinspired Systems), Lecture Notes in Computer Science, Vol. 3512
Journal: Proceedings of the 8th International Work-Conference on Artificial Neural Networks (Computational Intelligence and Bioinspired Systems)
Volume: LNCS 3512
Pages: 960-967
Year: 2005
Day: 0
Editors: J Cabestany and A Prieto and F Sandoval
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/11494669_118
Event Name: IWANN 2005
Event Place: Vilanova i la Geltrú, Barcelona, Spain

Address: Berlin Heidelberg, Germany
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex

@inproceedings{3357,
  title = {Long Term Prediction of Product Quality in a Glass Manufacturing Process Using a Kernel Based Approach},
  author = {Jung, T. and Herrera, L. and Sch{\"o}lkopf, B.},
  journal = {Proceedings of the 8th International Work-Conference on Artificial Neural Networks (Computational Intelligence and Bioinspired Systems)},
  booktitle = {Proceedings of the 8th International Work-Conferenceon Artificial Neural Networks (Computational Intelligence and Bioinspired Systems), Lecture Notes in Computer Science, Vol. 3512},
  volume = {LNCS 3512},
  pages = {960-967},
  editors = {J Cabestany and A Prieto and F Sandoval},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin Heidelberg, Germany},
  year = {2005}
}