Authors:
Jeremiah Corrigan
and
Jie Zhang
Affiliation:
School of Engineering, Merz Court, Newcastle University, Newcastle upon Tyne NE1 7RU and U.K.
Keyword(s):
Slow Feature Analysis, Neural Network, Soft Sensor, Dynamic Process Modelling, Data-driven Modelling.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
Abstract:
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.
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