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We introduce GP-FNARX: a new model for nonlinear system identification basedon a nonlinear autoregressive exogenous model (NARX) with filtered regressors(F) where the nonlinear regression problem is tackled using sparse Gaussianprocesses (GP). We integrate data pre-processing with system identificationinto a fully automated procedure that goes from raw data to an identifiedmodel. Both pre-processing parameters and GP hyper-parameters are tuned bymaximizing the marginal likelihood of the probabilistic model. We obtain aBayesian model of the system's dynamics which is able to report its uncertaintyin regions where the data is scarce. The automated approach, the modeling ofuncertainty and its relatively low computational cost make of GP-FNARX a goodcandidate for applications in robotics and adaptive control.

Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes