LSTM water prediction for feedforward control of moulding sand compressibility

Author:

Rose Alexander,Seel Alexander,Luck Bennet,Grotjahn Martin

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference17 articles.

1. Abadi, M. et al. (2015). Tensorflow: Large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/.

2. BDG (2019). BDG-Richtlinie R 311 Emission beim Einsatz von Formstoffbindemitteln und Formstoffu¨berzugsstoffen. BDG-Umweltausschuss. https://www.bdguss.de/fileadmin/contentbdguss/DerBDG/Richtlinien/R311.pdf.

3. Bergstra, J., Yamins, D., and Cox, D.D. (2013). Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. http://proceedings.mlr.press/v28/bergstra13.pdf.

4. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks;Gal,2016

5. Gemming, H. (2003). Prozesssicherheit in der Formstoffaufbereitung mit Hilfe der Fuzzy-Logik. Dissertation, Technische Universitt Freiberg. http://webdoc.sub.gwdg.de/ebook/serien/aa/FreibergerDissOnline/185.pdf.

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1. LSTM based Time-series Prediction for Optimal Scheduling in the Foundry Industry;2022 International Joint Conference on Neural Networks (IJCNN);2022-07-18

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