Controlling nonlinear dynamical systems into arbitrary states using machine learning
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2021/06/21
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Journal
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Indian UGC (journal)
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Refrences41
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Citations4
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Alexander Haluszczynski
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Christoph Räth
Abstract
Cite
Haluszczynski, Alexander, and Christoph Räth. “Controlling Nonlinear Dynamical Systems into Arbitrary States Using Machine Learning”. Scientific Reports, vol. 11, no. 1, 2021, https://doi.org/10.1038/s41598-021-92244-6.
Haluszczynski, A., & Räth, C. (2021). Controlling nonlinear dynamical systems into arbitrary states using machine learning. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-92244-6
Haluszczynski, Alexander, and Christoph Räth. “Controlling Nonlinear Dynamical Systems into Arbitrary States Using Machine Learning”. Scientific Reports 11, no. 1 (2021). https://doi.org/10.1038/s41598-021-92244-6.
Haluszczynski A, Räth C. Controlling nonlinear dynamical systems into arbitrary states using machine learning. Scientific Reports. 2021;11(1).
Journal Categories
Medicine
Science
Science
Science (General)
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Adding filters to improve reservoir computer performance | Physica D: Nonlinear Phenomena |
| 8 | 2021 |
| Robustness of LSTM neural networks for multi-step forecasting of chaotic time series | Chaos, Solitons & Fractals |
| 85 | 2020 |
| Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics | Neural Networks |
| 201 | 2020 |
| 10.1103/PhysRevLett.125.093901 | Physical Review Letters |
| 2020 | |
Breaking symmetries of the reservoir equations in echo state networks | Chaos: An Interdisciplinary Journal of Nonlinear Science |
| 21 | 2020 |
Refrences Analysis
The category
Science: Physics 18
is the most frequently represented among the references in this article. It primarily includes studies from Physical Review Letters and Nature. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Controlling chaotic maps using next-generation reservoir computing | Chaos: An Interdisciplinary Journal of Nonlinear Science |
| 2024 | |
Bayesian Inference of Recurrent Switching Linear Dynamical Systems with Higher-Order Dependence | Symmetry |
| 2024 | |
| A recurrent gated unit-based mixture kriging machine Bayesian filtering approach for long-term prediction of dynamic intermittency | IISE Transactions |
| 2023 | |
Closed-Loop Current Stimulation Feedback Control of a Neural Mass Model Using Reservoir Computing | Applied Sciences |
| 2023 |
Citations Analysis
The category
Technology: Engineering (General). Civil engineering (General) 2
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A recurrent gated unit-based mixture kriging machine Bayesian filtering approach for long-term prediction of dynamic intermittency and was published in 2023. The most recent citation comes from a 2024 study titled Bayesian Inference of Recurrent Switching Linear Dynamical Systems with Higher-Order Dependence. This article reached its peak citation in 2024, with 2 citations. It has been cited in 4 different journals, 50% of which are open access. Among related journals, the Symmetry cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.