TY - JOUR
T1 - Soft Sensing of Nonlinear and Multimode Processes Based on Semi-Supervised Weighted Gaussian Regression
AU - Shi, Xudong
AU - Kang, Qi
AU - Zhou, Meng Chu
AU - Abusorrah, Abdullah
AU - An, Jing
N1 - Funding Information:
Manuscript received May 15, 2020; accepted June 10, 2020. Date of publication June 22, 2020; date of current version October 2, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 51775385, Grant 61703279, and Grant 71371142, in part by the Strategy Research Project of Artificial Intelligence Algorithms of Ministry of Education of China, in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University under Grant RG-20-135-38, and in part by the Fundamental Research Funds for the Central Universities. The associate editor coordinating the review of this article and approving it for publication was Prof. Okyay Kaynak. (Corresponding authors: Qi Kang; MengChu Zhou.) Xudong Shi and Qi Kang are with the Department of Control Science and Engineering, Tongji University, Shanghai 201804, China, and also with the Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China (e-mail: xdshi@tongji.edu.cn; qkang@tongji.edu.cn).
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - This paper presents a new semi-supervised probabilistic density-based regression approach, called Semi-supervised Weighted Gaussian Regression (SWGR), for the soft sensing of nonlinear and multimode industrial processes given a limited number of labeled data samples. In SWGR, different weights are assigned to each training sample based on their similarities to a query sample. Then a local weighted Gaussian density is built for capturing the joint probability of historical samples around the query sample. The training process of parameters in SWGR incorporates both labeled and unlabeled data samples via a maximum likelihood estimation algorithm. In this way, the soft sensor model is able to approximate the nonlinear mechanics of input and output variables and remedy the insufficiency of labeled samples. At last, the output prediction as well as the uncertainty of prediction can be obtained by the conditional distribution. Two case studies validate that the proposed semi-supervised soft sensing method outperforms some recent methods.
AB - This paper presents a new semi-supervised probabilistic density-based regression approach, called Semi-supervised Weighted Gaussian Regression (SWGR), for the soft sensing of nonlinear and multimode industrial processes given a limited number of labeled data samples. In SWGR, different weights are assigned to each training sample based on their similarities to a query sample. Then a local weighted Gaussian density is built for capturing the joint probability of historical samples around the query sample. The training process of parameters in SWGR incorporates both labeled and unlabeled data samples via a maximum likelihood estimation algorithm. In this way, the soft sensor model is able to approximate the nonlinear mechanics of input and output variables and remedy the insufficiency of labeled samples. At last, the output prediction as well as the uncertainty of prediction can be obtained by the conditional distribution. Two case studies validate that the proposed semi-supervised soft sensing method outperforms some recent methods.
KW - Soft sensing
KW - just in time learning
KW - probabilistic density-based regression
KW - semi-supervised learning
KW - weighted Gaussian density
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U2 - 10.1109/JSEN.2020.3003826
DO - 10.1109/JSEN.2020.3003826
M3 - Article
AN - SCOPUS:85092581842
SN - 1530-437X
VL - 20
SP - 12950
EP - 12960
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 21
M1 - 9122561
ER -