Polynomial function error stair of reservoir computing and its applications in characterizing the learning capability-Reference-Cited by-同舟云学术

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Polynomial function error stair of reservoir computing and its applications in characterizing the learning capability

Published:2024-08-23 Issue: Volume: Page:
ISSN:0924-090X
Container-title:Nonlinear Dynamics
language:en
Short-container-title:Nonlinear Dyn

Author:

Jia Linyuan,Yan Zixiang,Zhao Hui,Gao Jian,Xiao Jinghua

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China,China

Opening Project of State Key Lab of Information Photonics and Optical Communications

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11071-024-10178-w.pdf

Reference42 articles.

1. Shi, Z., Han, M.: Ridge regression learning in esn for chaotic time series prediction. Control Decis. 22, 258 (2007)

2. Pathak, J., Lu, Z., Hunt, B.R., Girvan, M., Ott, E.: Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data. Chaos 27(12), 121102 (2017). https://doi.org/10.1063/1.5010300

3. Pathak, J., Wikner, A., Fussell, R., Chandra, S., Hunt, B.R., Girvan, M., Ott, E.: Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model. Chaos 28(4), 041101 (2018). https://doi.org/10.1063/1.5028373

4. Liu, S., Xiao, J., Yan, Z., Gao, J.: Noise resistance of next-generation reservoir computing: a comparative study with high-order correlation computation. Nonlinear Dyn. 111(15), 14295–14308 (2023). https://doi.org/10.1007/s11071-023-08592-7

5. Zhang, H., Fan, H., Wang, L., Wang, X.: Learning hamiltonian dynamics with reservoir computing. Phys. Rev. E 104, 024205 (2021). https://doi.org/10.1103/PhysRevE.104.024205

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