Multi-Channel Graph Convolutional Network based End-Point Element Composition Prediction of Converter Steelmaking

Author:

Chang Shuchao,Zhao Chunhui,Li Yuanlong,Zhou Min,Fu Chuan,Qiao Honglin

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference19 articles.

1. Atwood, J. and Towsley, D. (2016). Diffusion-Convolutional Neural Networks. Proceedings of the 30th International Conference on Neural Information Processing Systems, 2001-2009.

2. Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y. (2014). Spectral Networks and Locally Connected Networks on Graphs. 2nd International Conference on Learning Representations.

3. Defferrard, M., Bresson, X., and Vandergheynst, P. (2016). Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Proceedings of the 30th International Conference on Neural Information Processing Systems, 3844-3852.

4. The Converter Steelmaking End Point Prediction Model Based on RBF Neural Network;Dong;Applied Mechanics and Materials,2014

5. Dual attention-based encoder-decoder: A customized sequence-to-sequence learning for soft sensor development;Feng;IEEE Transactions on Neural Networks and Learning Systems,2020

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