Learning strange attractors with reservoir systems

Abstract

This paper shows that the celebrated Embedding Theorem of Takens is a particular case of a much more general statement according to which, randomly generated linear state-space representations of generic observations of an invertible dynamical system carry in their wake an embedding of the phase space dynamics into the chosen Euclidean state space. This embedding coincides with a natural generalized synchronization that arises in this setup and that yields a topological conjugacy between the state-space dynamics driven by the generic observations of the dynamical system and the dynamical system itself. This result provides additional tools for the representation, learning, and analysis of chaotic attractors and sheds additional light on the reservoir computing phenomenon that appears in the context of recurrent neural networks.

36 pages, 11 figures

References in corpus (2)

• Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data
• Next Generation Reservoir Computing

Cited by in corpus (8)

• Catch-22s of reservoir computing
• Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data
• Embedding Information onto a Dynamical System
• Reduced-order adaptive synchronization in a chaotic neural network with parameter mismatch: A dynamical system vs. machine learning approach
• Limits of Learning Dynamical Systems
• Thermodynamic limit in learning period three
• Input-dependence in quantum reservoir computing
• State-space systems as dynamic generative models