Combined Input Training and Radial Basis Function Neural Networks based Nonlinear Principal Components Analysis Model Applied for Process Monitoring
Messaoud Bouakkaz, Mohamed-Faouzi Harkat
2012
Abstract
In this paper a novel Nonlinear Principal Component Analysis (NLPCA) is proposed. Generally, a NLPCA model is performed by using two sub-models, mapping and demapping. The proposed NLPCA model consists of two cascade three-layer neural networks for mapping and demapping, respectively. The mapping model is identified by using a Radial Basis Function (RBF) neural networks and the demapping is performed by using an Input Training neural networks (IT-Net). The nonlinear principal components, which represents the desired output of the first network, are obtained by the IT-NET. The proposed approach is illustrated by a simulation example and then applied for fault detection and isolation of the TECP process.
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in Harvard Style
Bouakkaz M. and Harkat M. (2012). Combined Input Training and Radial Basis Function Neural Networks based Nonlinear Principal Components Analysis Model Applied for Process Monitoring . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 483-492. DOI: 10.5220/0004152304830492
in Bibtex Style
@conference{ncta12,
author={Messaoud Bouakkaz and Mohamed-Faouzi Harkat},
title={Combined Input Training and Radial Basis Function Neural Networks based Nonlinear Principal Components Analysis Model Applied for Process Monitoring},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={483-492},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004152304830492},
isbn={978-989-8565-33-4},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - Combined Input Training and Radial Basis Function Neural Networks based Nonlinear Principal Components Analysis Model Applied for Process Monitoring
SN - 978-989-8565-33-4
AU - Bouakkaz M.
AU - Harkat M.
PY - 2012
SP - 483
EP - 492
DO - 10.5220/0004152304830492