Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing-Reference-Cited by-同舟云学术

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Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing

Published:2019-10 Issue:10 Volume:29 Page:103143
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Haluszczynski Alexander¹²^ORCID,Räth Christoph³

Affiliation:

1. Department of Physics, Ludwig-Maximilians-Universität, Schellingstraße 4, 80799 Munich, Germany

2. risklab GmbH, Seidlstraße 24, 80335 Munich, Germany

3. Deutsches Zentrum für Luft- und Raumfahrt, Institut für Materialphysik im Weltraum, Münchner Str. 20, 82234 Wessling, Germany

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5118725

Reference29 articles.

1. Testing for nonlinearity in time series: the method of surrogate data

2. Reservoir observers: Model-free inference of unmeasured variables in chaotic systems

3. Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data

4. Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

5. Observing spatio-temporal dynamics of excitable media using reservoir computing

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1. Chaotic attractor reconstruction using small reservoirs—the influence of topology;Machine Learning: Science and Technology;2024-08-27

2. Reservoir computing for a MEMS mirror-based laser beam control on FPGA;Optical Review;2024-04

3. The connectivity degree controls the difficulty in reservoir design of random boolean networks;Frontiers in Computational Neuroscience;2024-03-14

4. Learning from the past: reservoir computing using delayed variables;Frontiers in Applied Mathematics and Statistics;2024-03-01

5. Universal Approximation of Linear Time-Invariant (LTI) Systems Through RNNs: Power of Randomness in Reservoir Computing;IEEE Journal of Selected Topics in Signal Processing;2024-03

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