Integrating dynamic slow feature analysis with neural networks for enhancing soft sensor performance
Integrating dynamic slow feature analysis with neural networks for enhancing soft sensor performance
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DOI：
10.1016/j.compchemeng.2020.106842
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发表时间：
2020-08-04
影响因子：
4.3
通讯作者：
Zhang, Jie
中科院分区：
文献类型：
--
作者：
Corrigan, Jeremiah;Zhang, Jie
本文提出了集成慢特征分析（SFA）与神经网络（SFA-NN）的软测量开发。将动态线性SFA应用于易测过程变量数据。然后选择占主导地位的慢特征作为神经网络的输入，以预测难以测量的产品质量变量。SFA可以通过提取缓慢变化的潜变量（称为慢特征）来捕获工业过程的潜在动态。提出了利用scree plot进行主导慢特征选择的方法。神经网络用于科普许多真实的工业过程中存在的非线性。在两个真实的工业过程中，该方法的有效性进行了评估，并与慢特征回归，偏最小二乘回归，传统的前馈神经网络，并使用主成分分析前的神经网络。建议的SFA-NN在这两个案例研究中，这些技术之间的最佳泛化性能。(c)2020爱思唯尔有限公司保留所有权利。
This paper proposes integrating slow feature analysis (SFA) with neural networks (SFA-NN) for soft sensor development. Dynamic linear SFA is applied to the easy to measure process variable data. Then the dominant slow features are selected as the inputs of a neural network to predict the difficult to measure product quality variables. SFA can capture underlying dynamics of industrial processes through the extraction of slowly varying latent variables, known as slow features. Selection of dominant slow features using scree plot is proposed. Neural networks are utilised to cope with nonlinearities present in many real industrial processes. The effectiveness of the proposed method is evaluated on two real industrial processes and is compared with slow feature regression, partial least square regression, traditional feed-forward neural networks, and using principal component analysis prior to a neural network. The proposed SFA-NN gives the best generalisation performance amongst these techniques in both case studies. (c) 2020 Elsevier Ltd. All rights reserved.