首页|基于低秩重构表示的动态回归迁移模型

基于低秩重构表示的动态回归迁移模型

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针对实际流程工业过程存在动态时变和概念漂移特性,导致软测量模型预测精度下降的问题,提出基于低秩重构表示的动态迁移回归模型.为了更好地描述动态过程,在动态内模型偏最小二乘框架下,将高维过程数据映射到低维潜变量空间中,以捕获质量变量与潜变量之间的动态相关性.为了减小概念漂移,在获得动态相关性的同时,通过增强不同工况质量变量估计值之间的相关性实现数据的条件分布对齐.在3个公开工业数据集上的实验结果表明:所提出模型的预测精度与静态基模型和动态基模型相比均有所提升,可以有效地提高模型的预测精度和泛化能力.
Dynamic transfer regression model based on low-rank reconstruction representation
Aiming at the problem of the actual process industry process with dynamic time-varying and concept drift characteristics,which leads to a decrease in the prediction accuracy of the soft sensor model,a dynamic regression migration model based on low-rank reconstruction representation is proposed.In order to better describe the dynamic process,under the dynamic internal model partial least squares framework,the high-dimensional process data is mapped to the low-dimensional latent variable space to capture the dynamic correlation between quality data and latent variables.In order to reduce concept drift,while obtaining dynamic correlation,the conditional distribution alignment of data is achieved by enhancing the correlation between the estimated values of quality variables in different working conditions.Compared with the static base model and the dynamic base model,the experimental results on the three public industrial datasets improved,indicating that the proposed method can effectively improve the prediction accuracy and generalization ability of the model.

soft sensordynamic time variationconcept driftdynamic-inner partial least squaresconditional distribution alignmentlow-rank reconstruction

霍海丹、阎高伟、程兰、任密蜂、肖舒怡

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太原理工大学电气与动力工程学院,太原 030024

软测量 动态时变 概念漂移 动态内模型偏最小二乘 条件分布对齐 低秩重构

国家自然科学基金面上项目国家自然科学基金面上项目国家自然科学基金面上项目山西省自然科学基金项目山西省自然科学基金项目格盟集团科技创新基金项目

619732266207323262003233201901D211083202103021231892022-05

2024

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子:1.227
ISSN:1001-0920
年,卷(期):2024.39(8)