Natural gradient algorithm for neural networks applied to non-linear high power amplifiers

Autor:	H. Abdulkader, Daniel Roviras, Francis Castanie, F. Langlet
Rok vydání:	2002
Předmět:	Artificial neural network Computer science Amplifier Equalization (audio) Predistortion Power (physics) Identification (information) Nonlinear system Control and Systems Engineering Signal Processing Convergence (routing) Electronic engineering Electrical and Electronic Engineering Algorithm
Zdroj:	International Journal of Adaptive Control and Signal Processing. 16:557-576
ISSN:	1099-1115 0890-6327
DOI:	10.1002/acs.725
Popis:	This paper investigates the processing techniques for non-linear high power amplifiers (HPA) using neural networks (NNs). Several applications are presented: Identification and Predistortion of the HPA. Various Neural Network structures are proposed to identify and predistort the HPA. Since a few decades, NNs have shown excellent performance in solving complex problems (like classification, recognition, etc.) but usually they suffer from slow convergence speed. Here, we propose to use the natural gradient instead of the classical ordinary gradient in order to enhance the convergence properties. Results are presented concerning identification and predistortion using classical and natural gradient. Practical implementations issues are given at the end of the paper. Copyright © 2002 John Wiley & Sons, Ltd.
Databáze:	OpenAIRE
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