Nonlinear Dimension Reduction Based Neural Modeling for Nonlinear Complex DPS

  • Han-Xiong Li
  • Chenkun Qi
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 50)

Abstract

A nonlinear principal component analysis (NL-PCA) based neural modeling approach is presented for a lower-order or more accurate solution for nonlinear distributed parameter systems (DPS). One NL-PCA network is trained for the nonlinear dimension reduction and the nonlinear time/space reconstruction. The other neural model is to learn the system dynamics with a linear/nonlinear separated model structure. With the powerful capability of dimension reduction and the intelligent learning, this approach can model the nonlinear complex DPS with much more complexity. The simulation on the catalytic rod and the experiment on the snap curing oven will demonstrate the effectiveness of the presented method.

Keywords

Radial Basis Function Radial Basis Function Network Distribute Parameter System Radial Basis Function Model Nonlinear Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Netherlands 2011

Authors and Affiliations

  • Han-Xiong Li
    • Chenkun Qi

      There are no affiliations available

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