Memory augmented echo state network for time series prediction
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09276-4.pdf
Reference24 articles.
1. Zhou L, Wang HW (2022) Multihorizons transfer strategy for continuous online prediction of time-series data in complex systems. Int J Intell Syst 37(10):7706–7735
2. Schafer AM, Zimmermann HG (2007) Recurrent neural networks are universal approximators. Int J Neural Syst 17(4):253–263
3. Jaeger H (2001) The ‘echo state’ approach to analysing and training recurrent neural networks-with an erratum note. German Natl Res Center Inf Technol GMD Techn Report 148(34):13
4. Li Y, Li FJ (2019) PSO-based growing echo state network. Appl Soft Comput 85:105774
5. Chen Q, Jin YC, Song YD (2022) Fault-tolerant adaptive tracking control of Euler-Lagrange systems—An echo state network approach driven by reinforcement learning. Neurocomputing 484:109–116
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