偏最小二乘回归
软传感器
钥匙(锁)
计算机科学
数据挖掘
过程(计算)
机器学习
推论
概率逻辑
贝叶斯推理
人工智能
自回归模型
期望最大化算法
模式识别(心理学)
贝叶斯概率
数学
统计
最大似然
操作系统
计算机安全
作者
Junhua Zheng,Zhihuan Song
标识
DOI:10.1016/j.jprocont.2019.09.007
摘要
Due to the difficulty in measuring key performance indices in the process, only a small portion of collected data may have values for both routinely recorded variables and key performance indices, while a large portion of data only has values for routinely recorded variables. In order to improve the performance of data-driven soft sensor modeling, the idea of semi-supervised learning is incorporated with the traditional partial least squares modeling method. Furthermore, the single semi-supervised model structure is extended to the mixture form, in order to handle more complex data characteristics. An efficient Expectation-Maximization algorithm is designed for model training. An industrial case study is presented for performance evaluation of the developed method, with a Bayesian inference approach developed for results integration of different local models.
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