基于概率选择的主动学习智能软测量建模

doi:10.16182/j.issn1004731x.joss.20-0093

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (6): 1350-1357.doi: 10.16182/j.issn1004731x.joss.20-0093

基于概率选择的主动学习智能软测量建模

代学志¹, 熊伟丽^1,2

1.江南大学物联网工程学院,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:2020-02-27 修回日期:2020-04-29 出版日期:2021-06-18 发布日期:2021-06-23
作者简介:代学志(1995-),男,硕士生,研究方向为复杂工业过程建模。E-mail：2735864271@qq.com
基金资助:
国家自然科学基金(61773182); 国家重点研发计划子课题(2018YFC1603705-03); 江苏高校“青蓝工程”

Active Learning Intelligent Soft Sensor based on Probability Selection

Dai Xuezhi¹, Xiong Weili^1,2

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

Received:2020-02-27 Revised:2020-04-29 Online:2021-06-18 Published:2021-06-23

摘要/Abstract

摘要： 针对复杂工业过程中有标签样本少,样本标记成本高等问题,提出一种基于概率选择的主动学习软测量建模方法。该方法首先采用主成分分析对无标签样本进行子空间集成;然后基于所有子学习器的输出,计算无标签样本的不确定度,从而对无标签样本进行信息评估,选取最有价值的样本进行人工标记;最后通过分析无标签样本和有标签样本的作用,引入训练集的性能指标完成对终止条件的设计。通过实际工业过程数据的应用仿真,验证了所提方法在减小标记代价的同时,还能够实现模型精度的提高。

关键词: 概率选择, 软测量, 子学习器, 不确定度, 终止条件

Abstract: Aiming at lack of tag samples and high cost of sampling tags in complex industrial processes, an active learning algorithm based on probability selection is proposed. Firstly, unlabeled samples are performed subspace integration by using the principal component analysis. Then, the information of unlabeled samples is evaluated by the uncertainty, which is calculated based on the out put of all sub learners. And the most valuable samples are selected to mark manually. Finally, the function of unlabeled samples and labeled samples are analyzed, and the termination conditions are designed by introducing the performance index of training set. Through simulations of industrial processes data, it is verified that the proposed method can improve the accuracy of the model while reducing the cost of marking.

Key words: probability selection, soft sensor, sub learners, uncertainty, termination conditions

中图分类号:

TP274

代学志, 熊伟丽. 基于概率选择的主动学习智能软测量建模[J]. 系统仿真学报, 2021, 33(6): 1350-1357.

Dai Xuezhi, Xiong Weili. Active Learning Intelligent Soft Sensor based on Probability Selection[J]. Journal of System Simulation, 2021, 33(6): 1350-1357.

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基于概率选择的主动学习智能软测量建模

Active Learning Intelligent Soft Sensor based on Probability Selection

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