Nonlinear Data-driven Process Modelling using Slow Feature Analysis and Neural Networks
Nonlinear Data-driven Process Modelling using Slow Feature Analysis and Neural Networks
复制标题
DOI：
10.5220/0007958904390446
复制
发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
Jeremiah Corrigan;Jie Zhang
中科院分区：
文献类型：
--
作者：
Jeremiah Corrigan;Jie Zhang
慢特征分析是一种从数据集中提取缓慢变化的潜在变量的技术。这些被称为慢特征的潜在变量在应用于数据处理时可以捕捉潜在的动态，当使用这些慢特征构建数据驱动模型时，可以改进泛化。为了提高非线性动态过程建模的泛化能力，提出了一种利用神经网络进行慢特征分析的方法。此外，还提出了一种利用慢度变化来选择主导慢速特征数目的方法。将提出的方法应用于工业聚合过程中估计聚合物熔体指数的软测量，以验证该方法的性能。将该方法与主成分分析神经网络和不含任何潜变量的神经网络方法进行了比较。工业应用的结果证明了该方法在提高模型泛化能力和降维方面的有效性。
Slow feature analysis is a technique that extracts slowly varying latent variables from a dataset. These latent variables, known as slow features, can capture underlying dynamics when applied to process data, leading to improved generalisation when a data-driven model is built with these slow features. A method utilising slow feature analysis with neural networks is proposed in this paper for improving generalisation in nonlinear dynamic process modelling. Additionally, a method for selecting the number of dominant slow features using changes in slowness is proposed. The proposed method is applied to creating a soft sensor for estimating polymer melt index in an industrial polymerisation process to validate the method’s performance. The proposed method is compared with principal component analysis-neural network and a neural network without any latent variable method. The results from this industrial application demonstrate the effectiveness of the proposed method for improving model generalisation capability and reducing dimensionality.