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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 Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003 (pp. 137-142).

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DCB1-5 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DCB2-3
Genre: Conference Paper

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Kocijan, J, Author
Banko B, Likar B, Girard A, Murray-Smith, R, Author
Rasmussen, CE1, Author           
Ruano, E.A., Editor
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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: Issued
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 Identifiers: BibTex Citekey: 2314
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Title: Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003
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Title: Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 137 - 142 Identifier: -