TR2022-066
Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models
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- "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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- "Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models", American Control Conference (ACC), June 2022.
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MERL Contacts:
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Research Areas:
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%.
Related News & Events
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NEWS MERL researchers presented 9 papers at the American Control Conference (ACC) Date: June 8, 2022 - June 10, 2022
Where: Atlanta, GA
MERL Contacts: Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Marcel Menner; Rien Quirynen; Abraham P. Vinod; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference in Atlanta, GA, MERL presented 9 papers on subjects including autonomous-vehicle decision making and motion planning, realtime Bayesian inference and learning, reference governors for hybrid systems, Bayesian optimization, and nonlinear control.