Interpretable Prediction Modeling for Froth Flotation via Stacked Graph Convolutional Network
Author:
Affiliation:
1. School of Automation, Central South University, Changsha, China
2. SDIC Xinjiang Lop Nur Potash Co., Ltd., Hami, China
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Science and Technology Innovation Program of Hunan Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Science Applications,Artificial Intelligence
Link
http://xplorestaging.ieee.org/ielx7/9078688/10384607/10026469.pdf?arnumber=10026469
Reference35 articles.
1. Data-driven Soft Sensing for Batch Processes Using Neural Network-based Deep Quality-Relevant Representation Learning
2. A Self-Interpretable Soft Sensor Based on Deep Learning and Multiple Attention Mechanism: From Data Selection to Sensor Modeling
3. LSTMED: An uneven dynamic process monitoring method based on LSTM and Autoencoder neural network
4. Soft Sensors Based on Adaptive Stacked Polymorphic Model for Silicon Content Prediction in Ironmaking Process
5. Artificial neural networks for water quality soft-sensing in wastewater treatment: a review
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