New Nonlinear Approach for Process Monitoring: Neural Component Analysis,Industrial & Engineering Chemistry Research

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New Nonlinear Approach for Process Monitoring: Neural Component Analysis
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2020-08-26 , DOI: 10.1021/acs.iecr.0c02256
Zhijiang Lou ₁ , Youqing Wang ₂

Affiliation

Nonlinearity is extremely common in industrial processes. For handling the nonlinearity problem, this paper combines artificial neural networks (ANN) with principal component analysis (PCA) and proposes a new neural component analysis (NCA). NCA has a similar network structure as ANN and adopts the gradient descent method for training, hence it has the same nonlinear fitting ability as ANN. Furthermore, NCA adopts PCA’s dimension reduction strategy to extract the uncorrelated components from the process data and constructs statistical indices for process monitoring. The simulation test results show that NCA can successfully extract the uncorrelated components from the nonlinear process data, and it has better performance than other nonlinear approaches.

中文翻译：

新的非线性过程监控方法：神经成分分析

非线性在工业过程中非常普遍。为了处理非线性问题，本文将人工神经网络（ANN）与主成分分析（PCA）相结合，并提出了一种新的神经成分分析（NCA）。NCA具有与ANN类似的网络结构，并采用梯度下降法进行训练，因此具有与ANN相同的非线性拟合能力。此外，NCA采用PCA的降维策略从流程数据中提取不相关的组件，并构建用于流程监控的统计指标。仿真测试结果表明，NCA可以成功地从非线性过程数据中提取不相关的分量，并且比其他非线性方法具有更好的性能。

更新日期：2020-08-26

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