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Variational system identification for nonlinear state-space models
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DOI：10.1016/j.automatica.2022.110687.png)
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Abstract
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This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational inference (VI) approach, which is a principled method that has deep connections to maximum likelihood estimation. This VI approach ultimately provides estimates of the model as solutions to an optimisation problem, which is deterministic, tractable and can be solved using standard optimisation tools. A specialisation of this approach for systems with additive Gaussian noise is also detailed. The proposed method is examined numerically on a range of simulated and real examples focusing on the robustness to parameter initialisation; additionally, favourable comparisons are performed against state-of-the-art alternatives.(c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
System identification
Variational inference
Nonlinear models
Parameter estimation
Assumed density
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