Designing chaotic attractors: A semisupervised approach
Chaotic dynamics are ubiquitous in nature and useful in engineering, but their geometric design can be challenging. Here we propose a method using reservoir computing to generate chaos with a desired shape by providing a periodic orbit as a template, called a skeleton. We exploit a bifurcation of the reservoir to intentionally induce unsuccessful training of the skeleton, revealing inherent chaos. The emergence of this untrained attractor, resulting from the interaction between the skeleton and the reservoir's intrinsic dynamics, offers a semisupervised framework for designing chaos.
Authors
- Yasuo Kuniyoshi (ORCID: https://orcid.org/0000-0001-8443-4161)
- Kazuyuki Aihara (ORCID: https://orcid.org/0000-0002-4602-9816)
- Kohei Nakajima (ORCID: https://orcid.org/0000-0001-5589-4054)
- Tempei Kabayama (ORCID: https://orcid.org/0009-0002-4733-1416)
Institutions
- The University of Tokyo (JP)
Publication Details
- Journal
- Physical review. E
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1103/physreve.111.034207
- Citations
- 6
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 14.89
Funders
- Japan Society for the Promotion of Science
- Japan Science and Technology Agency