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Open AccessArticle

Bayesian Identification of Dynamical Systems

1
School of Engineering and Information Technology, The University of New South Wales, Canberra ACT 2600, Australia
2
Laboratoire des signaux et systèmes (L2S), CentraleSupélec, 91192 Gif-sur-Yvette, France
3
Institut Pprime, 86073 Poitiers Cedex 9, France
4
Ambrosys GmbH, 14469 Potsdam, Germany
5
Institute for Physics and Astrophysics, University of Potsdam, 14469 Potsdam, Germany
*
Author to whom correspondence should be addressed.
Presented at the 39th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Garching, Germany, 30 June–5 July 2019.
Proceedings 2019, 33(1), 33; https://doi.org/10.3390/proceedings2019033033
Published: 12 February 2020
Many inference problems relate to a dynamical system, as represented by dx/dt = f (x), where x ∈ ℝn is the state vector and f is the (in general nonlinear) system function or model. Since the time of Newton, researchers have pondered the problem of system identification: how should the user accurately and efficiently identify the model f – including its functional family or parameter values – from discrete time-series data? For linear models, many methods are available including linear regression, the Kalman filter and autoregressive moving averages. For nonlinear models, an assortment of machine learning tools have been developed in recent years, usually based on neural network methods, or various classification or order reduction schemes. The first group, while very useful, provide “black box" solutions which are not readily adaptable to new situations, while the second group necessarily involve the sacrificing of resolution to achieve order reduction. To address this problem, we propose the use of an inverse Bayesian method for system identification from time-series data. For a system represented by a set of basis functions, this is shown to be mathematically identical to Tikhonov regularization, albeit with a clear theoretical justification for the residual and regularization terms, respectively as the negative logarithms of the likelihood and prior functions. This insight justifies the choice of regularization method, and can also be extended to access the full apparatus of the Bayesian inverse solution. Two Bayesian methods, based on the joint maximum a posteriori (JMAP) and variational Bayesian approximation (VBA), are demonstrated for the Lorenz equation system with added Gaussian noise, in comparison to the regularization method of least squares regression with thresholding (the SINDy algorithm). The Bayesian methods are also used to estimate the variances of the inferred parameters, thereby giving the estimated model error, providing an important advantage of the Bayesian approach over traditional regularization methods.
Keywords: Bayesian inverse problem; dynamical systems; system identification; regularization; sparsification Bayesian inverse problem; dynamical systems; system identification; regularization; sparsification
MDPI and ACS Style

Niven, R.K.; Mohammad-Djafari, A.; Cordier, L.; Abel, M.; Quade, M. Bayesian Identification of Dynamical Systems. Proceedings 2019, 33, 33.

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