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  • Research Article
  • Open Access

Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

EURASIP Journal on Advances in Signal Processing20032003:574805

Received: 13 December 2002

Published: 20 November 2003


We use natural gradient (NG) learning neural networks (NNs) for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM) procedure in terms of convergence speed and mean squared error (MSE) performance.


  • satellite communications
  • system identification
  • adaptive signal processing
  • neural networks

Authors’ Affiliations

Electrical and Computer Engineering Department, Queen's University, Kingston, Canada


© Copyright © 2003 Hindawi Publishing Corporation 2003