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

• 作者: Jun Cai Qiong Jia Shaojun Li Xinyi Jiang
• 来源期刊: 中国化学工程学报（英文版）
• 年，卷（期）: 2020,（12）
• 所属期刊栏目: Process Systems Engineering and Process Safety
• 页码范围: 3061-3069
• 页数: 9页
• 语种: 英文

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 sub-datasets 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.