Robust Sparse Gaussian Process Regression for Soft Sensing in Industrial Big Data Under the Outlier Condition-Reference-Cited by-同舟云学术

Robust Sparse Gaussian Process Regression for Soft Sensing in Industrial Big Data Under the Outlier Condition

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Huang Haojie¹^ORCID,Peng Xin²^ORCID,Du Wei²^ORCID,Zhong Weimin³^ORCID

Affiliation:

1. College of Electrical Engineering and Automation and the Key Laboratory of Industrial Automation Control Technology and Information Processing, Fuzhou University, Fuzhou, China

2. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

3. Key Laboratory of Smart Manufacturing in Energy Chemical Process and the Engineering Research Center of Process System Engineering, Ministry of Education, East China University of Science and Technology, Shanghai, China

Funder

National Natural Science Fund for Distinguished Young Scholars

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10458968.pdf?arnumber=10458968

Reference40 articles.

1. A Sparse Nonstationary Trigonometric Gaussian Process Regression and Its Application on Nitrogen Oxide Prediction of the Diesel Engine

2. Soft Sensor Modeling of Nonlinear Industrial Processes Based on Weighted Probabilistic Projection Regression

3. Robust Variational Bayesian-Based Soft Sensor Model for LPV Processes With Delayed and Integrated Output Measurements

4. Variable-Scale Probabilistic Just-in-Time Learning for Soft Sensor Development with Missing Data

5. Data-driven Soft Sensors in the process industry

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