An Efficient Composite Deep Latent Feature Learning Framework With Layerwise Random Mapping for Complicated Industrial Soft Sensor Modeling
Author:
Affiliation:
1. College of Control Science and Engineering, China University of Petroleum, Qingdao, China
2. College of Control Science and Engineering and the College of Mechanical and Electronic Engineering, China University of Petroelum, Qingdao, China
Funder
Qingdao Natural Science Foundation
Shandong Provincial Natural Science Foundation of China
Opening Fund of National Engineering Research Center of Marine Geophysical Prospecting and Exploration and Development Equipment
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/7361/10660640/10616016.pdf?arnumber=10616016
Reference33 articles.
1. A Novel Bidirectional Long Short-Term Memory Network With Weighted Attention Mechanism for Industrial Soft Sensor Development
2. A review of just‐in‐time learning‐based soft sensor in industrial process
3. Attention-Based Interval Aided Networks for Data Modeling of Heterogeneous Sampling Sequences With Missing Values in Process Industry
4. Memory-Adaptive Supervised LSTM Networks for Deep Soft Sensor Development of Industrial Processes
5. Temperature prediction for roller kiln based on hybrid first-principle model and data-driven MW-DLWKPCR model
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