[1]何罗苏阳,熊伟丽.助训练框架下的半监督软测量建模方法[J].智能系统学报,2023,18(2):231-239.[doi:10.11992/tis.202111019]
HE Luosuyang,XIONG Weili.Semi-supervised soft sensor modeling method under the help-training framework[J].CAAI Transactions on Intelligent Systems,2023,18(2):231-239.[doi:10.11992/tis.202111019]
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HE Luosuyang,XIONG Weili.Semi-supervised soft sensor modeling method under the help-training framework[J].CAAI Transactions on Intelligent Systems,2023,18(2):231-239.[doi:10.11992/tis.202111019]
助训练框架下的半监督软测量建模方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第2期
页码:
231-239
栏目:
学术论文—机器学习
出版日期:
2023-05-05
- Title:
- Semi-supervised soft sensor modeling method under the help-training framework
- Keywords:
- soft sensor modeling; semi-supervised; help-training; twin support vector regression; K-nearest neighbor; confidence; learner; debutanizer
- 分类号:
- TP274
- 摘要:
- 为了充分利用工业过程中大量无标签样本信息，并减少过程的不确定因素对无标签样本质量的影响，提出一种助训练框架下的半监督孪生支持向量回归软测量建模方法。采用孪生支持向量回归机构建主学习器，对高置信度无标签样本添加伪标签；同时，基于K近邻算法构建辅学习器，最大化学习器在近邻样本集上的均方误差，经过此项指标筛选后的待处理样本集包含了更多的数据信息；主、辅学习器二者相辅相成，一定程度上提高了模型的泛化性；再利用所构建的助训练框架提高样本利用率后得到预测模型，实现对无标签样本信息的充分挖掘。通过对脱丁烷塔工业过程中的实际数据进行建模仿真，所得结果表明此模型具有良好的预测性能。
- Abstract:
- A semi-supervised twin support vector regression soft sensor modeling method under the help-training framework is proposed to maximize a large number of unlabeled sample information in industrial processes and reduce the impact of process uncertainties on the quality of unlabeled samples. The twin support vector regression is used to build the main learner and add pseudo labels to the unlabeled samples with the highest confidence. Simultaneously, the auxiliary learner is constructed on the basis of the K-nearest neighbor algorithm to maximize the root mean square error of the learner on the nearest neighbor sample set. The candidate sample set screened by this index contains additional data information. The main and auxiliary learners complement each other, which improves the generalization of the model to a certain extent. The prediction model is then obtained by using the help-training framework to improve the sample utilization to mine the unlabeled sample information. Results show that the model has good prediction performance based on the modeling and simulation of the real data in the industrial process of debutanizer.
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备注/Memo
收稿日期:2021-11-12。
基金项目:国家自然科学基金项目（61773182）；国家重点研发计划子课题（2018YFC1603705-03）.
作者简介:何罗苏阳,硕士研究生,主要研究方向为复杂工业过程建模;熊伟丽,教授,博士生导师,主要研究方向为复杂工业过程建模与监控、智能软测量技术。主持国家自然科学基金面上项目、国家自然科学基金青年项目、江苏省产学研等省部级以上纵向项目10项,授权发明专利26项,获得中国商业联合会科技进步一等奖1项,发表学术论文近百篇
通讯作者:熊伟丽. E-mail:greenpre@163.com
更新日期/Last Update:
1900-01-01