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.
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)