Adaptive control for multi-scale stochastic dynamical systems with stochastic next generation reservoir computing Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2505.09327
The rapid advancement of neuroscience and machine learning has established data-driven stochastic dynamical system modeling as a powerful tool for understanding and controlling high-dimensional, spatio-temporal processes. We introduce the stochastic next-generation reservoir computing (NG-RC) controller, a framework that integrates the computational efficiency of NG-RC with stochastic analysis to enable robust event-triggered control in multiscale stochastic systems. The asymptotic stability of the controller is rigorously proven via an extended stochastic LaSalle theorem, providing theoretical guarantees for amplitude regulation in nonlinear stochastic dynamics. Numerical experiments on a stochastic Van-der-Pol system subject to both additive and multiplicative noise validate the algorithm, demonstrating its convergence rate across varying temporal scales and noise intensities. To bridge theoretical insights with real-world applications, we deploy the controller to modulate pathological dynamics reconstructed from epileptic EEG data. This work advances a theoretically guaranteed scalable framework for adaptive control of stochastic systems, with broad potential for data-driven decision making in engineering, neuroscience, and beyond.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2505.09327
- https://arxiv.org/pdf/2505.09327
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4414939300
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414939300Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2505.09327Digital Object Identifier
- Title
-
Adaptive control for multi-scale stochastic dynamical systems with stochastic next generation reservoir computingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-14Full publication date if available
- Authors
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Joanna J. Cheng, Ting Gao, Jinqiao DuanList of authors in order
- Landing page
-
https://arxiv.org/abs/2505.09327Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2505.09327Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2505.09327Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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