CUED Publications database

Integrated pre-processing for bayesian nonlinear system identification with gaussian processes

Frigola, R and Rasmussen, CE (2013) Integrated pre-processing for bayesian nonlinear system identification with gaussian processes. Proceedings of the IEEE Conference on Decision and Control. pp. 5371-5376. ISSN 0191-2216

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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. © 2013 IEEE.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:16
Last Modified: 07 Sep 2017 01:44
DOI: