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Training of Neural Network Wiener Models with Recursive Prediction Error Algorithm

  • Andrzej Janczak
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)

Abstract

Identification of the Wiener system consisting of a linear dynamics in series with a static nonlinearity is considered. A recursive prediction error training algorithm for a recurrent neural network Wiener model is proposed. The gradient of the model output w.r.t. parameters of its linear part is computed with the sensitivity method. The proposed algorithm has superior convergence properties in comparison with gradient methods such as the sensitivity method or the truncated backpropagation through time. Its performance is illustrated with a simulated example of a pneumatic valve.

Keywords

Recurrent Neural Network Gradient Descent Method Nonlinear Element Linear Dynamic Model Pneumatic Valve 
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 2003

Authors and Affiliations

  • Andrzej Janczak
    • 1
  1. 1.Institute of Control and Computation EngineeringUniversity of Zielona GóraZielona GóraPoland

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