Gaussian Process Models for Systems Identification
نویسنده
چکیده
Different models can be used for nonlinear dynamic systems identification and the Gaussian process model is a relatively new option with several interesting features: model predictions contain the measure of confidence, the model has a small number of training parameters and facilitated structure determination, and different possibilities of including prior knowledge exist. In this paper the framework for the identification of a dynamic system model based on the Gaussian processes is presented and a short survey with a comprehensive bibliography of published works on application of Gaussian processes for modelling of dynamic systems is given.
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