Active training sample selection and updating strategy for near-infrared model with an industrial application

Kaixun He; Kai Wang; Yayun Yan

doi:10.1016/j.cjche.2019.02.018

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Active training sample selection and updating strategy for near-infrared model with an industrial application

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Active training sample selection and updating strategy for near-infrared model with an industrial application
- Chinese Journal of Chemical Engineering Vol. 27, Issue 11, Pages: 2749-2758(2019)
- Affiliations：
  
  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology,Qingdao,China,266590
  2. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2019.02.018
  CLC：
- Published：2019
- Accepted：
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Kaixun He, Kai Wang, Yayun Yan. Active training sample selection and updating strategy for near-infrared model with an industrial application[J]. Chinese Journal of Chemical Engineering, 2019, 27(11): 2749-2758.
DOI：

Kaixun He, Kai Wang, Yayun Yan. Active training sample selection and updating strategy for near-infrared model with an industrial application[J]. Chinese Journal of Chemical Engineering, 2019, 27(11): 2749-2758. DOI： 10.1016/j.cjche.2019.02.018.

摘要

Abstract

Training sample selection is widely accepted as an important step in developing a near-infrared (NIR) spectroscopic model. For industrial applications

the initial training dataset is usually selected empirically. This process is time-consuming

and updating the structure of the modeling dataset online is difficult. Considering the static structure of the modeling dataset

the performance of the established NIR model could be degraded in the online process. To cope with this issue

an active training sample selection and updating strategy is proposed in this work. The advantage of the proposed approach is that it can select suitable modeling samples automatically according to the process information. Moreover

it can adjust model coefficients in a timely manner and avoid arbitrary updating effectively. The effectiveness of the proposed method is validated by applying the method to an industrial gasoline blending process.

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