基于核极限学习机的快速主动学习方法及其软测量应用

代学志; 熊伟丽

doi:10.11949/0438-1157.20200260

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基于核极限学习机的快速主动学习方法及其软测量应用

过程系统工程 | 更新时间：2026-07-24

- 基于核极限学习机的快速主动学习方法及其软测量应用
- A fast active learning method based on kernel extreme learning machine and its application for soft sensing
- 化工学报 2020年71卷第11期页码：5226-5236
- 作者机构：
  
  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
- 作者简介：
  
  代学志（1995—），男，硕士研究生，2735864271@qq.com
  熊伟丽（1978—），女，教授，greenpre@163.com
- 基金信息：
  
  国家自然科学基金项目(61773182);国家重点研发计划子课题(2018YFC1603705-03);江苏高校 “青蓝工程”资助项目
- DOI：10.11949/0438-1157.20200260
  中图分类号： TP 274
- 收稿：2020-03-16，
  
  修回：2020-06-29，
  
  网络首发：2020-11-03，
  
  纸质出版：2020-11-05
- 稿件说明：
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代学志, 熊伟丽. 基于核极限学习机的快速主动学习方法及其软测量应用[J]. 化工学报, 2020, 71(11): 5226-5236

DAI Xuezhi, XIONG Weili. A fast active learning method based on kernel extreme learning machine and its application for soft sensing[J]. CIESC Journal, 2020, 71(11): 5226-5236
代学志, 熊伟丽. 基于核极限学习机的快速主动学习方法及其软测量应用[J]. 化工学报, 2020, 71(11): 5226-5236 DOI： 10.11949/0438-1157.20200260.

DAI Xuezhi, XIONG Weili. A fast active learning method based on kernel extreme learning machine and its application for soft sensing[J]. CIESC Journal, 2020, 71(11): 5226-5236 DOI： 10.11949/0438-1157.20200260.

摘要

为提高主动学习方法的运行效率和降低人工标记成本，提出一种基于核极限学习机的快速主动学习方法，并将其应用于软测量建模中。首先，采用核极限学习机对无标记样本进行信息评估，将无标记样本的置信度作为样本选择评价准则，选择对改善模型性能最有价值的无标记样本进行标记；其次，充分考虑每次迭代过程的运算信息，引入矩阵反演公式优化样本选择策略，提升迭代过程样本评估的运行效率；最后，应用矩阵相似度理论对迭代过程的已标记样本数据进行信息度量，并将其作为迭代终止依据，以最小的标记代价提升模型性能。将所提方法应用于硫回收过程H

S和SO

浓度软测量研究中，仿真结果表明：所提方法不仅标记代价小，而且提高了迭代的快速性，比较全面地提升了主动学习算法的性能。通过开展本研究工作，为少标记样本情况下的软测量技术应用提供了一种新方法。

Abstract

To improve the efficiency of active learning and reduce the cost of manual marking

a fast active learning method based on kernel extreme learning machine is proposed and applied to soft sensor research. Firstly

the information of unlabeled samples is evaluated by kernel extreme learning machine

and the confidence of unlabeled samples is taken as the evaluation criterion of sample selection. The most valuable unlabeled samples to improve the performance of the model are selected for labeling. Secondly

considering the operation information of each iteration process

the matrix inversion formula is introduced to optimize the sample selection strategy to improve the efficiency of sample evaluation. Finally

the matrix similarity theory is applied to measure the information of the labeled sample data in the iterative process

and it is used as the basis for the termination of the iterative process to improve the performance of the model with the minimum cost of labeling. The proposed method is applied to the study of H

S and SO

concentration soft sensor in the sulfur recovery process. The simulation results show that the proposed method not only has low marking cost

but also improves the speed of iteration and the perf

ormance of active learning algorithm. By carrying out this research work

a new method is provided for the application of soft-sensing technology under the condition of less labeled samples.

关键词

Keywords

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