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A case based comparison of identification with neural network and Gaussian process models.

2003

Conference Paper

ei


In this paper an alternative approach to black-box identification of non-linear dynamic systems is compared with the more established approach of using artificial neural networks. The Gaussian process prior approach is a representative of non-parametric modelling approaches. It was compared on a pH process modelling case study. The purpose of modelling was to use the model for control design. The comparison revealed that even though Gaussian process models can be effectively used for modelling dynamic systems caution has to be axercised when signals are selected.

Author(s): Kocijan, J. and Banko, B. and Likar, B. and Girard, A. and Murray-Smith, R. and Rasmussen, CE.
Journal: Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003
Volume: 1
Pages: 137-142
Year: 2003
Month: April
Day: 0
Editors: Ruano, E.A.

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

Event Name: Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{2314,
  title = {A case based comparison of identification with neural network and Gaussian process models.},
  author = {Kocijan, J. and Banko, B. and Likar, B. and Girard, A. and Murray-Smith, R. and Rasmussen, CE.},
  journal = {Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003},
  volume = {1},
  pages = {137-142},
  editors = {Ruano, E.A.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = apr,
  year = {2003},
  month_numeric = {4}
}