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A System Identification Method Based on Multi-layer Perception and Model Extraction

  • Chang Hu
  • Li Cao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3174)

Abstract

Artificial Neural Networks (ANNs) have provided an interesting and labor-saving approach for system identification. However, ANNs fall short when an explicit model is needed. In this paper, a method of getting the explicit model by extracting it from a trained ANN is proposed. To identify a system, a Multi-Layer Perceptron (MLP) is constructed, trained and a polynomial model is extracted from the trained network. This method is tested in the experiments and shows its capability for system identification, compared with the Least Squared method.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Chang Hu
    • 1
  • Li Cao
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingP.R.China

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