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 communicationssystem identificationadaptive signal processingneural networks

Authors’ Affiliations

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


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