Nonlinear dimension reduction based neural modeling for distributed parameter processes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

28 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)4164-4170
Journal / PublicationChemical Engineering Science
Volume64
Issue number19
Publication statusPublished - 1 Oct 2009

Abstract

Many chemical processes are nonlinear distributed parameter systems with unknown uncertainties. For this class of infinite-dimensional systems, the low-order model identification from process data is very important in practice. The dimension reduction with a principal component analysis (PCA) is only a linear approximation for nonlinear problem. In this study, a nonlinear dimension reduction based low-order neural model identification approach is proposed for nonlinear distributed parameter processes. First, a nonlinear principal component analysis (NL-PCA) network is designed for the nonlinear dimension reduction, which can transform the high-dimensional spatio-temporal data into a low-dimensional time domain. Then, a neural system can be easily identified to model this low-dimensional temporal data. Finally, the spatio-temporal dynamics can be reproduced using the nonlinear time/space reconstruction. The simulations on a typical nonlinear transport-reaction process show that the proposed approach can achieve a better performance than the linear PCA based modeling approach. © 2009 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Dimension reduction, Distributed parameter system, Low-order modeling, Neural network, Nonlinear principal component analysis, Transport-reaction process