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Dynamic systems identification with Gaussian processes

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This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) prior model. This model is an example of the use of a probabilistic non-parametric modelling approach. GPs are flexible models capable of modelling complex nonlinear systems. Also, an attractive feature of this model is that the variance associated with the model response is readily obtained, and it can be used to highlight areas of the input space where prediction quality is poor, owing to the lack of data or complexity (high variance). We illustrate the GP modelling technique on a simulated example of a nonlinear system.

Keywords: Gaussian processes; Nonlinear dynamic systems; System identification

Document Type: Research Article


Affiliations: 1: Hamilton Institute, Natural University of Ireland, Maynooth, Ireland 2: Department of Computer Science, University of Glasgow, Glasgow, UK 3: Jozef Stefan Institute, Ljubljana, Slovenia

Publication date: 2005-12-01

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