TR2022-066

Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models


    •  Berntorp, K., Menner, M., "Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models", American Control Conference (ACC), June 2022.
      BibTeX TR2022-066 PDF
      • @inproceedings{Berntorp2022jun,
      • author = {Berntorp, Karl and Menner, Marcel},
      • title = {Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models},
      • booktitle = {American Control Conference (ACC)},
      • year = 2022,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2022-066}
      • }
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  • Research Areas:

    Control, Dynamical Systems, Machine Learning

Abstract:

Recent research has shown that it is possible to perform online learning of nonlinear dynamical systems. Furthermore, the results suggest that combining approximate Gaussian-process (GP) regression with model-based estimators, such as Kalman filters and particle filters (PFs), leads to efficient learners under the GP-state-space model (GP-SSM) framework. Here, we analyze how learning of GP-SSMs can be done when there are constraints on the system to be learned. Our analysis is based on a recently developed online PF-based learning method, where the GP-SSM is expressed as a basis-function expansion. We show that the method by adaptation of the basis functions can satisfy several constraints, such as symmetry, antisymmetry, Neumann boundary conditions, and linear operator constraints. A Monte-Carlo simulation study indicates reduced estimation errors with more than 50%.

 

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