Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes
Abstract
We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP). We integrate data pre-processing with system identification into a fully automated procedure that goes from raw data to an identified model. Both pre-processing parameters and GP hyper-parameters are tuned by maximizing the marginal likelihood of the probabilistic model. We obtain a Bayesian model of the system's dynamics which is able to report its uncertainty in regions where the data is scarce. The automated approach, the modeling of uncertainty and its relatively low computational cost make of GP-FNARX a good candidate for applications in robotics and adaptive control.
Keywords
Cite
@article{arxiv.1303.2912,
title = {Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes},
author = {Roger Frigola and Carl Edward Rasmussen},
journal= {arXiv preprint arXiv:1303.2912},
year = {2013}
}
Comments
Proceedings of the 52th IEEE International Conference on Decision and Control (CDC), Firenze, Italy, December 2013