Echo state networks trained by Tikhonov least squares are \(L^2(\mu)\) approximators of ergodic dynamical systems

DOI10.1016/j.physd.2021.132882zbMath1491.37075arXiv2005.06967OpenAlexW3024515584WikidataQ114141965 ScholiaQ114141965MaRDI QIDQ2077652

Allen Hart, James Hook, Jonathan H. P. Dawes

Publication date: 21 February 2022

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.06967

zbMATH Keywords

Lorenz equationstime series analysisrecurrent neural networksreservoir computingliquid state machinedelay embedding

Mathematics Subject Classification ID

Time series analysis of dynamical systems (37M10) Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.) (37M25)

Related Items (7)

Predicting turbulent dynamics with the convolutional autoencoder echo state network ⋮ Generalised synchronisations, embeddings, and approximations for continuous time reservoir computers ⋮ Learning Theory for Dynamical Systems ⋮ Learning strange attractors with reservoir systems ⋮ Reservoir Computing with an Inertial Form ⋮ Dimension reduction in recurrent networks by canonicalization ⋮ Symmetry kills the square in a multifunctional reservoir computer

Uses Software

• RODES

Cites Work

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