A subspace ensemble regression model based slow feature for soft sensing application

Qiong Jia; Jun Cai; Xinyi Jiang; Shaojun Li

doi:10.1016/j.cjche.2020.07.047

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A subspace ensemble regression model based slow feature for soft sensing application

Process Systems Engineering and Process Safety | Updated：2026-01-06

- A subspace ensemble regression model based slow feature for soft sensing application
- A subspace ensemble regression model based slow feature for soft sensing application
- 中国化学工程学报（英文版） 2020年28卷第12期页码：3061-3069
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2020.07.047
  中图分类号：
- 纸质出版：2020
- Accepted：
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Qiong Jia, Jun Cai, Xinyi Jiang, 等. A subspace ensemble regression model based slow feature for soft sensing application[J]. 中国化学工程学报（英文版）, 2020,28(12):3061-3069.

Qiong Jia, Jun Cai, Xinyi Jiang, Shaojun Li. A subspace ensemble regression model based slow feature for soft sensing application[J]. Chinese Journal of Chemical Engineering, 2020, 28(12): 3061-3069.
Qiong Jia, Jun Cai, Xinyi Jiang, 等. A subspace ensemble regression model based slow feature for soft sensing application[J]. 中国化学工程学报（英文版）, 2020,28(12):3061-3069. DOI： 10.1016/j.cjche.2020.07.047.

Qiong Jia, Jun Cai, Xinyi Jiang, Shaojun Li. A subspace ensemble regression model based slow feature for soft sensing application[J]. Chinese Journal of Chemical Engineering, 2020, 28(12): 3061-3069. DOI： 10.1016/j.cjche.2020.07.047.

摘要

Abstract

A novel adaptive subspace ensemble slow feature regression model was developed for soft sensing application. Compared to traditional single models and random subspace models

the proposed method is improved in three aspects. Firstly

sub-datasets are constructed through slow feature directions and variables in each subdatasets are selected according to the output related importance index. Then

an adaptive slow feature regression is presented for sub-models. Finally

a Bayesian inference strategy based on a slow feature analysis process that monitors statistics is developed for probabilistic combination. Two industrial examples were used to evaluate the proposed method.

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