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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2311.00369 |
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| _version_ | 1866909273981714432 |
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| author | da Mata, João Victor Galvão Hansson, Anders Andersen, Martin S. |
| author_facet | da Mata, João Victor Galvão Hansson, Anders Andersen, Martin S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_00369 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Direct System Identification of Dynamical Networks with Partial Measurements: a Maximum Likelihood Approach da Mata, João Victor Galvão Hansson, Anders Andersen, Martin S. Systems and Control Optimization and Control 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. |
| title | Direct System Identification of Dynamical Networks with Partial Measurements: a Maximum Likelihood Approach |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2311.00369 |