软传感器
过度拟合
贝叶斯概率
特征(语言学)
质量(理念)
计算机科学
贝叶斯推理
约束(计算机辅助设计)
回归
过程(计算)
聚合
数据挖掘
人工智能
数学
材料科学
统计
聚合物
人工神经网络
复合材料
哲学
操作系统
认识论
语言学
几何学
作者
Miao Zhang,Le Zhou,Jing Jie,Xiaoli Wu
摘要
Data-driven soft sensors are widely used to predict quality indices in propylene polymerization processes to improve the availability of measurements and efficiency. To deal with the nonlinearity and dynamics in propylene polymerization processes, a novel soft sensor based on quality-relevant slow feature analysis and Bayesian regression is proposed in this paper. The proposed method can handle the dynamics of the process better by extracting quality-relevant slow features, which present both the slowly varying characteristic and the correlations with quality indices. Meanwhile, a Bayesian inference model is developed to predict the quality indices, which takes advantages of a probability framework with iterative maximum likelihood techniques for parameter estimation and a sparse constraint for avoiding overfitting. Finally, a case study is conducted with data sampled from a practical industrial propylene polymerization process to demonstrate the effectiveness and superiority of the proposed method.
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