控制理论与应用2024，Vol.41Issue(3)：396-406,11.DOI:10.7641/CTA.2023.20661
基于超图正则化的域适应偏最小二乘多工况软测量模型
Multi-condition soft sensor modeling of domain adaptation partial least squares based on hypergraph regularization
摘要
Abstract
Multiple conditions in industrial processes can lead to changes in data distribution,which in turn can cause traditional soft sensor models to become inaccurate.Therefore,this paper proposes a domain-adaptive multi-conditions soft sensor regression model framework based on the hypergraph regularization.First,the nonlinear iterative partial least squares algorithm is used as the basic model to reconstruct the current condition data by using historical condition data in the latent variable space,to enhance the correlation between conditions and effectively reduce the differences in data distribution;Meanwhile,a low-rank sparsity constraint is imposed on the reconstructed coefficients to preserve the local and global subspace structure of the data;Secondly,the domain-adaptive latent variable solving process is constrained by the hypergraph regularterm,which effectively avoids the data structure being destroyed in the process of searching for latent variables.Finally,the model parameters are optimized by using the alternating direction multiplier method.Experiments on multiple datasets show that the method can effectively improve the prediction accuracy and generalization performance of the soft sensor model under multiple working conditions.关键词
多工况/超图/结构保持/域适应/软测量Key words
multiple working conditions/hypergraph/structure preservation/domain adaptation/soft sensor引用本文复制引用
霍海丹,阎高伟,王芳,任密蜂,程兰,李荣..基于超图正则化的域适应偏最小二乘多工况软测量模型[J].控制理论与应用,2024,41(3):396-406,11.基金项目
国家自然科学基金项目(61973226,62073232),山西省自然科学基金项目(20210302123189),山西省重点研发计划项目(201903D121143)资助.Supported by the National Natural Science Foundation of China(61973226,62073232),the National Natural Science Foundation of Shanxi Province(20210302123189)and the Shanxi Provincial Key Research and Development Project(201903D121143). （61973226,62073232）
