Open Access

Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

EURASIP Journal on Advances in Signal Processing20032003:574805

DOI: 10.1155/S1110865703306079

Received: 13 December 2002

Published: 20 November 2003

Abstract

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.

Keywords

satellite communications system identification adaptive signal processing neural networks

Authors’ Affiliations

(1)
Electrical and Computer Engineering Department, Queen's University

Copyright

© Copyright © 2003 Hindawi Publishing Corporation 2003