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All issues Volume 185 (2020) E3S Web Conf., 185 (2020) 02025 References
Open Access
Issue
E3S Web Conf.
Volume 185, 2020
2020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
Article Number 02025
Number of page(s) 4
Section Energy Saving and Environmental Protection Technology
DOI https://doi.org/10.1051/e3sconf/202018502025
Published online 01 September 2020
  1. Samanta B. Prediction of chaotic time series using computational intelligence[J]. Expert Systems with Applications, 2011, 38(9): 11406–11411. [Google Scholar]
  2. Jian-Cheng S, Ya-Tong Z, Jian-Guo L. Prediction of chaotic systems with multidimensional recurrent least squares support vector machines[J]. Chinese Physics, 2006, 15(6): 1208. [CrossRef] [Google Scholar]
  3. Pathak J, Hunt B, Girvan M, et al. Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach[J]. Physical review letters, 2018, 120(2): 024102. [CrossRef] [PubMed] [Google Scholar]
  4. Tél T, Gruiz M. Chaotic dynamics: an introduction based on classical mechanics[M]. Cambridge University Press, 2006. [Google Scholar]
  5. Laws of chaos: invariant measures and dynamical systems in one dimension[M]. Springer Science & Business Media, 2012. [Google Scholar]
  6. Quantum chaos: between order and disorder[M]. Cambridge University Press, 2006. [Google Scholar]
  7. Peitgen H O, Jürgens H, Saupe D. Chaos and fractals: new frontiers of science[M]. Springer Science & Business Media, 2006. [Google Scholar]
  8. Powers J G, Klemp J B, Skamarock W C, et al. The weather research and forecasting model: Overview, system efforts, and future directions[J]. Bulletin of the American Meteorological Society, 2017, 98(8): 1717–1737. [Google Scholar]
  9. Zhang R H, Shen X S. On the development of the GRAPES—A new generation of the national operational NWP system in China[J]. Chinese Science Bulletin, 2008, 53(22): 3429–3432. [Google Scholar]
  10. Chen D H, Xue J S, Yang X S, et al. New generation of multi-scale NWP system (GRAPES): general scientific design[J]. Chinese Science Bulletin, 2008, 53(22): 3433–3445. [Google Scholar]
  11. Kay J E, Deser C, Phillips A, et al. The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability[J]. Bulletin of the American Meteorological Society, 2015, 96(8): 1333–1349. [Google Scholar]
  12. Kusiak A, Zhang Z. Short-horizon prediction of wind power: A data-driven approach[J]. IEEE Transactions on Energy Conversion, 2010, 25(4): 1112–1122. [CrossRef] [Google Scholar]
  13. Zio E, Di Maio F, Stasi M. A data-driven approach for predicting failure scenarios in nuclear systems[J]. Annals of Nuclear Energy, 2010, 37(4): 482–491. [Google Scholar]
  14. Formentin S, Karimi A. A data-driven approach to mixed-sensitivity control with application to an active suspension system[J]. IEEE Transactions on Industrial Informatics, 2012, 9(4): 2293–2300. [Google Scholar]
  15. Szegedy C, Toshev A, Erhan D. Deep neural networks for object detection[C]//Advances in neural information processing systems. 2013: 2553–2561. [Google Scholar]
  16. Yegnanarayana B. Artificial neural networks[M]. PHI Learning Pvt. Ltd., 2009. [Google Scholar]
  17. Nielsen M A. Neural networks and deep learning[M]. San Francisco, CA: Determination press, 2015. [Google Scholar]
  18. Nelles O. Nonlinear system identification: from classical approaches to neural networks and fuzzy models[M]. Springer Science & Business Media, 2013. [Google Scholar]
  19. Saxe A M, McClelland J L, Ganguli S. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks[J]. arXiv preprint arXiv:1312.6120, 2013. [Google Scholar]
  20. Pan Y, Wang J. Model predictive control of unknown nonlinear dynamical systems based on recurrent neural networks[J]. IEEE Transactions on Industrial Electronics, 2011, 59(8): 3089–3101. [CrossRef] [Google Scholar]
  21. Evensen G. Data assimilation: the ensemble Kalman filter[M]. Springer Science & Business Media, 2009. [Google Scholar]
  22. Reichle R H. Data assimilation methods in the Earth sciences[J]. Advances in water resources, 2008, 31(11): 1411–1418. [Google Scholar]
  23. Goodfellow I, Bengio Y, Courville A. Deep learning[M]. MIT press, 2016. [Google Scholar]
  24. LeCun Y, Bengio Y, Hinton G. Deep learning[J]. nature, 2015, 521(7553): 436–444. [CrossRef] [PubMed] [Google Scholar]
  25. Bocquet M. Ensemble Kalman filtering without the intrinsic need for inflation[J]. 2011. [Google Scholar]
  26. Bocquet M, Raanes P N, Hannart A. Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation[J]. Nonlinear Processes in Geophysics, 2015, 22(6): 645. [Google Scholar]
  27. Fertig E J, Harlim J, Hunt B R. A comparative study of 4D-VAR and a 4D ensemble Kalman filter: Perfect model simulations with Lorenz-96[J]. Tellus A: Dynamic Meteorology and Oceanography, 2007, 59(1): 96–100. [CrossRef] [Google Scholar]
  28. Ramsundar B, Zadeh R B. TensorFlow for deep learning: from linear regression to reinforcement learning[M]. “ O’Reilly Media, Inc.”, 2018. [Google Scholar]

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