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Nonlinear dimension reduction based neural modeling for distributed parameter processes
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DOI：10.1016/j.ces.2009.06.053.png)
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Abstract
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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. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Distributed parameter system
Low-order modeling
Dimension reduction
Nonlinear principal component analysis
Neural network
Transport-reaction process
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