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

Kocijan, J., Banko, B., Likar, B., Girard, A., Murray-Smith, R., & Rasmussen, C. (2003). A case based comparison of identification with neural network and Gaussian process models. In E. Ruano (Ed.), Intelligent Control Systems and Signal Processing: ICONS 2003 (pp. 137-142).

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 Creators:
Kocijan, J, Author
Banko, B, Author
Likar, B, Author
Girard, A, Author
Murray-Smith, R, Author
Rasmussen, CE1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2003-04
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 2314
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Title: IFAC International Conference on Intelligent Control Systems and Signal Processing (ICONS 2003)
Place of Event: Faro, Portugal
Start-/End Date: 2003-04-08 - 2003-04-11

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Title: Intelligent Control Systems and Signal Processing: ICONS 2003
Source Genre: Proceedings
 Creator(s):
Ruano, EA, Editor
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Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 137 - 142 Identifier: -