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Nonlinear soft sensor development for industrial thickeners using domain transfer functional-link neural network
Control Engineering Practice ( IF 5.4 ) Pub Date : 2021-05-25 , DOI: 10.1016/j.conengprac.2021.104853
Runda Jia , Shulei Zhang , Fengqi You

The thickener is used to provide slurries with a stable and satisfactory concentration in the ore dressing plant. To efficiently control an industrial thickener, a soft sensor model should be built first to predict the underflow concentration. In industrial sites, it is usually expensive and time-consuming to collect sufficient high-quality data to develop a data-driven model. In this work, a nonlinear regression method for transfer learning is proposed to solve this problem, which is named domain transfer functional-link neural network (DT-FLNN). The framework of the proposed method includes two stages, and the issue of domain adaption is separately considered at each stage. In the first stage, the activation matrix of the source domain is reconstructed to narrow the distribution difference, and the augmented input matrices of the source and target domains are formulated. Then, the latent variable (LV) based linear regression method for transfer learning is performed at the second stage to train the FLNN of the target domain, and the task of domain adaption is realized by introducing a regularization term. Besides, a systematic method is also presented to determine the hyper-parameters in the proposed DT-FLNN method. The efficiency of the proposed method is evaluated by employing a numerical example and an industrial application. Compared with other nonlinear regression approaches for transfer learning, the proposed method can further increase the prediction accuracy and reduce the influence of noise.



中文翻译:

基于域转移功能链接神经网络的工业增稠剂非线性软传感器开发

所述增稠剂用于在选矿厂中提供具有稳定且令人满意的浓度的浆料。为了有效地控制工业浓缩机,应首先建立一个软传感器模型来预测底流浓度。在工业站点中,收集足够的高质量数据以开发数据驱动的模型通常是昂贵且费时的。在这项工作中,提出了一种用于转移学习的非线性回归方法来解决该问题,该方法称为域转移功能链接神经网络(DT-FLNN)。所提出的方法的框架包括两个阶段,并且在每个阶段分别考虑域自适应的问题。在第一阶段,重建源域的激活矩阵以缩小分布差异,并制定了源域和目标域的扩充输入矩阵。然后,在第二阶段执行基于潜变量(LV)的线性回归方法进行迁移学习,以训练目标域的FLNN,并通过引入正则项来实现域自适应的任务。此外,还提出了一种系统的方法来确定所提出的DT-FLNN方法中的超参数。通过数值例子和工业应用评估了所提出方法的效率。与其他用于迁移学习的非线性回归方法相比,该方法可以进一步提高预测精度,减少噪声的影响。在第二阶段执行基于潜变量(LV)的线性回归方法进行迁移学习,以训练目标域的FLNN,并通过引入正则项来实现域自适应的任务。此外,还提出了一种系统的方法来确定所提出的DT-FLNN方法中的超参数。通过数值例子和工业应用评估了所提出方法的效率。与其他用于迁移学习的非线性回归方法相比,该方法可以进一步提高预测精度,减少噪声的影响。在第二阶段执行基于潜变量(LV)的线性回归方法进行迁移学习,以训练目标域的FLNN,并通过引入正则项来实现域自适应的任务。此外,还提出了一种系统的方法来确定所提出的DT-FLNN方法中的超参数。通过数值例子和工业应用评估了所提出方法的效率。与其他用于迁移学习的非线性回归方法相比,该方法可以进一步提高预测精度,减少噪声的影响。还提出了一种系统的方法来确定所提出的DT-FLNN方法中的超参数。通过数值例子和工业应用评估了所提出方法的效率。与其他用于迁移学习的非线性回归方法相比,该方法可以进一步提高预测精度,减少噪声的影响。还提出了一种系统的方法来确定所提出的DT-FLNN方法中的超参数。通过数值例子和工业应用评估了所提出方法的效率。与其他用于迁移学习的非线性回归方法相比,该方法可以进一步提高预测精度,减少噪声的影响。

更新日期:2021-05-25
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