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

作     者：Qiong Jia Jun Cai Xinyi Jiang Shaojun Li Qiong Jia;Jun Cai;Xinyi Jiang;Shaojun Li

作者机构：Key Laboratory of Advanced Control and Optimization for Chemical ProcessesEast China University of Science and TechnologyMinistry of EducationShanghai 200237China 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2020年第28卷第12期

页      面：3061-3069页

核心收录：

学科分类：0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 07[理学] 0817[工学-化学工程与技术] 0703[理学-化学] 

基　　金：the support from the National Natural Science Foundation of China(No.21676086). 

主　　题：Soft sensing Slow feature regression Subspace modeling Ensemble learning 

摘      要：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.