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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DCB1-5
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.