Online real-time learning of dynamical systems from noisy streaming data
Abstract Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-49045-w |
| _version_ | 1797377063957561344 |
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| author | S. Sinha S. P. Nandanoori D. A. Barajas-Solano |
| author_facet | S. Sinha S. P. Nandanoori D. A. Barajas-Solano |
| author_sort | S. Sinha |
| collection | DOAJ |
| description | Abstract Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: (a) it allows for online real-time monitoring of a dynamical system; (b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; (c) it is computationally fast and less intensive than the popular extended dynamic mode decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the chaotic attractor of the Henon map, the IEEE 68 bus system, and a ring network of Van der Pol oscillators. |
| first_indexed | 2024-03-08T19:47:38Z |
| format | Article |
| id | doaj.art-a583e8d1fa2f4343a5f05d9c24622437 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T19:47:38Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-a583e8d1fa2f4343a5f05d9c246224372023-12-24T12:15:31ZengNature PortfolioScientific Reports2045-23222023-12-0113111510.1038/s41598-023-49045-wOnline real-time learning of dynamical systems from noisy streaming dataS. Sinha0S. P. Nandanoori1D. A. Barajas-Solano2Pacific Northwest National LaboratoryPacific Northwest National LaboratoryPacific Northwest National LaboratoryAbstract Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: (a) it allows for online real-time monitoring of a dynamical system; (b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; (c) it is computationally fast and less intensive than the popular extended dynamic mode decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the chaotic attractor of the Henon map, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.https://doi.org/10.1038/s41598-023-49045-w |
| spellingShingle | S. Sinha S. P. Nandanoori D. A. Barajas-Solano Online real-time learning of dynamical systems from noisy streaming data Scientific Reports |
| title | Online real-time learning of dynamical systems from noisy streaming data |
| title_full | Online real-time learning of dynamical systems from noisy streaming data |
| title_fullStr | Online real-time learning of dynamical systems from noisy streaming data |
| title_full_unstemmed | Online real-time learning of dynamical systems from noisy streaming data |
| title_short | Online real-time learning of dynamical systems from noisy streaming data |
| title_sort | online real time learning of dynamical systems from noisy streaming data |
| url | https://doi.org/10.1038/s41598-023-49045-w |
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