Journal of Central South University

, Volume 26, Issue 12, pp 3338–3350 | Cite as

RVFLN-based online adaptive semi-supervised learning algorithm with application to product quality estimation of industrial processes

  • Wei Dai (代伟)Email author
  • Jin-cheng Hu (胡金成)
  • Yu-hu Cheng (程玉虎)
  • Xue-song Wang (王雪松)
  • Tian-you Chai (柴天佑)


Direct online measurement on product quality of industrial processes is difficult to be realized, which leads to a large number of unlabeled samples in modeling data. Therefore, it needs to employ semi-supervised learning (SSL) method to establish the soft sensor model of product quality. Considering the slow time-varying characteristic of industrial processes, the model parameters should be updated smoothly. According to this characteristic, this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network (RVFLN), denoted as OAS-RVFLN. By introducing a L2-fusion term that can be seen a weight deviation constraint, the proposed algorithm unifies the offline and online learning, and achieves smoothness of model parameter update. Empirical evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and accuracy. Finally, the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product, which further verifies its effectiveness and potential of industrial application.

Key words

semi-supervised learning (SSL) L2-fusion term online adaptation random vector functional link network (RVFLN) 

基于随机权神经网络的在线自适应半监督学习算法 及其在工业过程产品质量评价中的应用


工业过程的产品质量往往难以在线检测,导致建模样本中存在大量的无标记样本,因此建立产 品质量软测量模型需要采用半监督学习(SSL)方法。工业过程通常具有慢时变特性,因此所建立的数 据模型也应随之改变参数,且具有一定的平滑性。针对这一特性,基于随机权神经网络RVFLN,提 出了一种新颖在线自适应半监督学习算法,即OAS-RVFLN。所提算法通过引入L2 融合项,采用权值 偏差约束,将离线和在线学习相统一,并使模型参数的优化问题得到平滑性。在基准测试函数和数据 集上的实验研究表明所提出的OAS-RVFLN 在学习速度和精度均优于传统方法;并将其应用于煤炭工 业的重介质选煤过程,估计产品灰分,进一步验证了其有效性以及工业应用的潜力。


半监督学习 L2 融合项 在线自适应 随机权神经网络 


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Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information and Control EngineeringChina University of Mining and TechnologyXuzhouChina
  2. 2.State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyangChina

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