Related references
Note: Only part of the references are listed.A Novel Soft Sensor Modeling Approach Based on Difference-LSTM for Complex Industrial Process
Jiayi Zhou et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)
Gated Stacked Target-Related Autoencoder: A Novel Deep Feature Extraction and Layerwise Ensemble Method for Industrial Soft Sensor Application
Qingqiang Sun et al.
IEEE TRANSACTIONS ON CYBERNETICS (2022)
A Supervised Bidirectional Long Short-Term Memory Network for Data-Driven Dynamic Soft Sensor Modeling
Chun Fai Lui et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2022)
Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development
Xiaofeng Yuan et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)
Deep learning with nonlocal and local structure preserving stacked autoencoder for soft sensor in industrial processes
Chenliang Liu et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)
Virtual Sensing f-CaO Content of Cement Clinker Based on Incremental Deep Dynamic Features Extracting and Transferring Model
Le Yao et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)
Data-Driven Batch-End Quality Modeling and Monitoring Based on Optimized Sparse Partial Least Squares
Qingchao Jiang et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)
Optimal Weighting Distance-Based Similarity for Locally Weighted PLS Modeling
Xinmin Zhang et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2020)
Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy
Xiaofeng Yuan et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Automatic Deep Extraction of Robust Dynamic Features for Industrial Big Data Modeling and Soft Sensor Application
Xinyu Zhang et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Dynamic Probabilistic Latent Variable Model for Process Data Modeling and Regression Application
Zhiqiang Ge et al.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2019)
Feature Extraction of Constrained Dynamic Latent Variables
Yanjun Ma et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network
Xiaofeng Yuan et al.
IEEE Transactions on Industrial Informatics (2019)
Weighted Linear Dynamic System for Feature Representation and Soft Sensor Application in Nonlinear Dynamic Industrial Processes
Xiaofeng Yuan et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application
Le Yao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application
Weiwu Yan et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2017)
Data-driven soft sensor development based on deep learning technique
Chao Shang et al.
JOURNAL OF PROCESS CONTROL (2014)
ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process
J. C. B. Gonzaga et al.
COMPUTERS & CHEMICAL ENGINEERING (2009)
Data-driven Soft Sensors in the process industry
Petr Kadlec et al.
COMPUTERS & CHEMICAL ENGINEERING (2009)
Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process
Pierantonio Facco et al.
JOURNAL OF PROCESS CONTROL (2009)
Statistical monitoring of dynamic multivariate processes - Part 1. Modeling autocorrelation and cross-correlation
L Xie et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2006)
Inferential control system of distillation compositions using dynamic partial least squares regression
M Kano et al.
JOURNAL OF PROCESS CONTROL (2000)
Share Paper
