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arXiv · 2311.00369

Direct System Identification of Dynamical Networks with Partial Measurements: a Maximum Likelihood Approach

João Victor Galvão da Mata·Anders Hansson·Martin S. Andersen
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Abstract

This paper introduces a novel direct approach to system identification of dynamic networks with missing data based on maximum likelihood estimation. Dynamic networks generally present a singular probability density function, which poses a challenge in the estimation of their parameters. By leveraging knowledge about the network's interconnections, we show that it is possible to transform the problem into a more tractable form by applying linear transformations. This results in a nonsingular probability density function, enabling the application of maximum likelihood estimation techniques. Our preliminary numerical results suggest that when combined with global optimization algorithms or a suitable initialization strategy, we are able to obtain a good estimate of the dynamics of the internal systems.

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João Victor Galvão da Mata, Anders Hansson, Martin S. Andersen. 2023-11-01. Direct System Identification of Dynamical Networks with Partial Measurements: a Maximum Likelihood Approach. https://doi.org/10.23919/ecc64448.2024.10590797

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