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Nonlinear Predictive Control Based on Wavelet Neural Network Applied to Polypropylene Process

  • Xiaohua Xia
  • Zhiyan Luan
  • Dexian Huang
  • Yihui Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

Abstract

A nonlinear adaptive predictive control strategy using orthogonal wavelet network model is presented. Based on a set of orthogonal wavelet functions, wavelet neural network performs a nonlinear mapping from the network input space to the wavelons output space in hidden layer. Its weight coefficients can be simply estimated by a linear least-square estimation algorithm. The excellent statistic properties of the weight parameters of wavelet network also can be obtained. A single input single output (SISO) nonlinear predictive control strategy is implemented in the simulation of a Polypropylene process.

Keywords

Model Predictive Control Wavelet Neural Network Orthogonal Wavelet Wavelet Network Single Input Single Output 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xiaohua Xia
    • 1
  • Zhiyan Luan
    • 1
  • Dexian Huang
    • 1
  • Yihui Jin
    • 1
  1. 1.Process Control Lab, Automation DepartmentTsinghua UniversityBeijingChina

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