Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes

By Roger Frigola, Carl E. Rasmussen. 2013

50 citations recorded by the source corpus.

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.

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1. Yew-Soon Ong
2. Haitao Liu
3. Jianfei Cai
4. Xiaobo Shen
5. Hung-yi Lee
6. Fan Yang
7. S. Mei
8. Min Liu

Citations in the corpus

• When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, methodology): “To address this issue, we could (i) conduct data-preprocessing to remove the noise from data [221]; (ii) adopt GPs considering input noise [200]; and (iii) employ the more powerful state space models (SSM) [217] introduced below.”
• Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting (2019, methodology): “Traditional methods such as Autoregressive (AR) [8] models and Gaussian Process (GP) [9] may fail.”
• Temporal pattern attention for multivariate time series forecasting (2018, methodology): “For that reason, substantial effort has been made for non-linear models for time series forecasting based on kernel methods (Chen, Wang, and Harris 2008), ensembles (Bouchachia and Bouchachia 2008), or Gaussian processes (Frigola and Rasmussen 2014).”