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Reservoir computers are powerful tools for chaotic time series prediction.
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2017
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I. Estébanez, I. Fischer, and M. C. Soriano, “Constructive Role of Noise for High-Quality Replication of Chaotic Attractor Dynamics Using a Hardware-Based Reservoir Computer,” Physical Review Applied 12
2019
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A. Haluszczynski and C. Räth, “Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing,” Chaos: An Interdisciplinary Journal of Nonlinear Science 29
2019
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L. Gonon and J.-P. Ortega, “Reservoir Computing Universality With Stochastic Inputs,” IEEE Transactions on Neural Networks and Learning Systems 31
2020
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X. Chen, T. Weng, H. Yang, C. Gu, J. Zhang, and M. Small, “Mapping topological characteristics of dynamical systems into neural networks: A reservoir computing approach,” Physical Review E 102
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A. Haluszczynski, J. Aumeier, J. Herteux, and C. Räth, “Reducing network size and improving prediction stability of reservoir computing,” Chaos: An Interdisciplinary Journal of Nonlinear Science 30
2020
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Q. Zhu, H. Ma, and W. Lin, “Detecting unstable periodic orbits based only on time series: When adaptive delayed feedback control meets reservoir computing,” Chaos: An Interdisciplinary Journal of Nonlinear Science 29
2019
Cited alongside, same era.
S. Krishnagopal, G. Katz, M. Girvan, and J. Reggia, “Encoding of a Chaotic Attractor in a Reservoir Computer: A Directional Fiber Investigation,” in 2019 International Joint Conference on Neural Networks (IJCNN) (2019) pp. 1–8
2019
Cited alongside, same era.
T. Qin, K. Wu, and D. Xiu, “Data driven governing equations approximation using deep neural networks,” Journal of Computational Physics 395
2019
Cited alongside, same era.
C. Sanderson and R. Curtin, “Armadillo: A template-based C++ library for linear algebra,” , 7
Cited in the paper.
D. J. Gauthier, E. Bollt, A. Griffith, and W. A. S. Barbosa, “Next generation reservoir computing,” Nature Communications 12
2021
Closest in time.
J. Z. Kim, Z. Lu, E. Nozari, G. J. Pappas, and D. S. Bassett, “Teaching recurrent neural networks to infer global temporal structure from local examples,” Nature Machine Intelligence 3
2021
Closest in time.