English

Designing Chaotic Attractors: A Semi-supervised Approach

Neural and Evolutionary Computing 2024-07-16 v1 Machine Learning Dynamical Systems Chaotic Dynamics

Abstract

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 novel semi-supervised framework for designing chaos.

Keywords

Cite

@article{arxiv.2407.09545,
  title  = {Designing Chaotic Attractors: A Semi-supervised Approach},
  author = {Tempei Kabayama and Yasuo Kuniyoshi and Kazuyuki Aihara and Kohei Nakajima},
  journal= {arXiv preprint arXiv:2407.09545},
  year   = {2024}
}

Comments

6 pages, 4 figures (excluding supplementary material)

R2 v1 2026-06-28T17:39:08.999Z