A novel second-order learning algorithm based attention-LSTM model for dynamic chemical process modeling
Author:
Funder
the Strategic Cooperation Technology Projects of CNPC and CUPB
the National Key Research and Development Project
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-03515-2.pdf
Reference42 articles.
1. Fang CZ, Xiao DY (1988) Processing identification. Tsinghua University Press, Beijing
2. Shardt YAW, Huang B (2013) Data quality assessment of routine operating data for process identification. Comput Chem Eng 55:19–27. https://doi.org/10.1016/j.compchemeng.2013.03.029
3. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
4. Ke WS, Huang DX, Yang F, Jiang YH (2017) Soft sensor development and applications based on LSTM in deep neural networks. In: IEEE symposium series on computational intelligence (SSCI), pp 1–6
5. Zhang X, Zou YY, Li SY, Xu SH (2019) A weighted auto regressive LSTM based approach for chemical processes modeling. Neurocomputing 367:64–74. https://doi.org/10.1016/j.neucom.2019.08.006
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