基于EM算法的半监督局部加权PLS在线建模方法

doi:10.16182/j.issn1004731x.joss.201801002

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (1): 8-10.doi: 10.16182/j.issn1004731x.joss.201801002

基于EM算法的半监督局部加权PLS在线建模方法

熊伟丽^1,2, 薛明晨¹, 李妍君¹

1.江南大学物联网工程学院自动化研究所,江苏无锡 214122;
2.江南大学轻工过程先进控制教育部重点实验室,江苏无锡 214122

收稿日期:2015-11-17 发布日期:2019-01-02
作者简介:熊伟丽(1978-),女,河南洛阳,博士,教授,硕导,研究方向为复杂工业过程建模及优化,智能优化算法及应用。
基金资助:
国家自然科学基金(61773182),江苏省“六大人才高峰”计划(2013-DZXX-043)

Online Modeling with Semi-Supervised Locally Weighted Partial Least Squares Based on Expectation Maximization Algorithm

Xiong Weili^1,2, Xue Mingchen¹, Li Yanjun¹

1.School of Internet of Things Engineering, Institute of Automation, Jiangnan University, Wuxi 214122, China;
2.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China

Received:2015-11-17 Published:2019-01-02

摘要/Abstract

摘要： 针对化工过程采样分析获得的有标签样本数量较少的问题,提出一种基于半监督学习的局部加权偏最小二乘在线软测量建模方法。将过程收集到的有标签及无标签训练样本放入同一数据库中;对于在线测得的新数据点,计算其与数据库中各样本点之间的相似度,将其作为各数据点的权重;建立半监督局部加权偏最小二乘在线软测量模型,并采用EM(Expectation Maximization)算法估计模型的参数,得到模型的在线预测输出。通过对脱丁烷塔过程的仿真研究,验证了所提方法具有良好的预测精度和泛化性能。

关键词: 半监督, 局部加权偏最小二乘, EM算法, 在线建模

Abstract: As only small proportion of labeled data can be obtained from chemical processes, an online soft sensing modeling method based on semi-supervised locally weighted partial least squares is proposed . The labeled and unlabeled historical data are accumulated to construct training database. The similarity between the newly measured data and the sample points in the database are calculated and used as the weight of each data point. The semi-supervised locally weighted partial least squares model is constructed, and the Expectation Maximization (EM) algorithm is employed to estimate the parameters of the model. Online prediction achieves simulation results of debutanizer distillation processes, which suggests that the proposed method has good prediction accuracy and stable generalization performance.

Key words: semi-supervised, locally weighted partial least squares, EM algorithm, online modeling

中图分类号:

TP391

熊伟丽, 薛明晨, 李妍君. 基于EM算法的半监督局部加权PLS在线建模方法[J]. 系统仿真学报, 2018, 30(1): 8-10.

Xiong Weili, Xue Mingchen, Li Yanjun. Online Modeling with Semi-Supervised Locally Weighted Partial Least Squares Based on Expectation Maximization Algorithm[J]. Journal of System Simulation, 2018, 30(1): 8-10.

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基于EM算法的半监督局部加权PLS在线建模方法

Online Modeling with Semi-Supervised Locally Weighted Partial Least Squares Based on Expectation Maximization Algorithm

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