Nonlinear Data-driven Process Modelling using Slow Feature Analysis and Neural Networks

Jeremiah Corrigan, Jie Zhang

2019

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.

Paper Citation

in Harvard Style

Corrigan J. and Zhang J. (2019). Nonlinear Data-driven Process Modelling using Slow Feature Analysis and Neural Networks.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 439-446. DOI: 10.5220/0007958904390446

in Bibtex Style

@conference{icinco19,
author={Jeremiah Corrigan and Jie Zhang},
title={Nonlinear Data-driven Process Modelling using Slow Feature Analysis and Neural Networks},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={439-446},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007958904390446},
isbn={978-989-758-380-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Nonlinear Data-driven Process Modelling using Slow Feature Analysis and Neural Networks
SN - 978-989-758-380-3
AU - Corrigan J.
AU - Zhang J.
PY - 2019
SP - 439
EP - 446
DO - 10.5220/0007958904390446