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.

Key words: 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.

关键词: Soft sensing, Slow feature regression, Subspace modeling, Ensemble learning