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Lookup NU author(s): Jeremiah Corrigan, Dr Jie ZhangORCiD
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Slow feature analysis is a technique that extracts slowly varying latent variables from a dataset. These latentvariables, known as slow features, can capture underlying dynamics when applied to process data, leading toimproved generalisation when a data-driven model is built with these slow features. A method utilising slowfeature analysis with neural networks is proposed in this paper for improving generalisation in nonlineardynamic process modelling. Additionally, a method for selecting the number of dominant slow features usingchanges in slowness is proposed. The proposed method is applied to creating a soft sensor for estimatingpolymer melt index in an industrial polymerisation process to validate the method’s performance. Theproposed method is compared with principal component analysis-neural network and a neural networkwithout any latent variable method. The results from this industrial application demonstrate the effectivenessof the proposed method for improving model generalisation capability and reducing dimensionality.
Author(s): Corrigan J, Zhang J
Editor(s): Oleg Gusikhin, Kurosh Madani, Janan Zaytoon
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019)
Year of Conference: 2019
Number of Volumes: 2
Pages: 439-446
Online publication date: 29/07/2019
Acceptance date: 23/05/2019
Publisher: SciTePress
URL: http://insticc.org/node/TechnicalProgram/icinco/presentationDetails/79589
Library holdings: Search Newcastle University Library for this item
ISBN: 9789897583803
