Controlling chaotic maps using next-generation reservoir computing.
| Autor: | Kent RM; Department of Physics, The Ohio State University, 191 W. Woodruff Ave., Columbus, Ohio 43210, USA., Barbosa WAS; Department of Physics, The Ohio State University, 191 W. Woodruff Ave., Columbus, Ohio 43210, USA., Gauthier DJ; Department of Physics, The Ohio State University, 191 W. Woodruff Ave., Columbus, Ohio 43210, USA.; ResCon Technologies, LLC, 1275 Kinnear Rd., Suite 238, Columbus, Ohio 43212, USA. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Chaos (Woodbury, N.Y.) [Chaos] 2024 Feb 01; Vol. 34 (2). |
| DOI: | 10.1063/5.0165864 |
| Abstrakt: | In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the controller in a series of control tasks for the chaotic Hénon map, including controlling the system between unstable fixed points, stabilizing the system to higher order periodic orbits, and to an arbitrary desired state. We show that our controller succeeds in these tasks, requires only ten data points for training, can control the system to a desired trajectory in a single iteration, and is robust to noise and modeling error. (© 2024 Author(s). Published under an exclusive license by AIP Publishing.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|