Combined input training and radial basis function neural networks based nonlinear principal components analysis model applied for process monitoring

Messaoud Bouakkaz, Mohamed-Faouzi Harkat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence
Pages483-492
Number of pages10
Publication statusPublished - 1 Dec 2012
Externally publishedYes
Event4th International Joint Conference on Computational Intelligence, IJCCI 2012 - Barcelona, Spain
Duration: 5 Oct 20127 Oct 2012

Other

Other4th International Joint Conference on Computational Intelligence, IJCCI 2012
CountrySpain
CityBarcelona
Period5/10/127/10/12

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Keywords

  • Fault Detection and Isolation
  • IT-net
  • Nonlinear PCA
  • Process Monitoring
  • RBF-neural Network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Bouakkaz, M., & Harkat, M-F. (2012). Combined input training and radial basis function neural networks based nonlinear principal components analysis model applied for process monitoring. In IJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence (pp. 483-492)