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Complex radial basis function networks trained by QR‐decomposition recursive least square algorithms applied in behavioral modeling of nonlinear power amplifiers
Author(s) -
Li Mingyu,
He Songbai,
Li Xiaodong
Publication year - 2009
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.20387
Subject(s) - qr decomposition , radial basis function , algorithm , weighting , nonlinear system , recursive least squares filter , computer science , robustness (evolution) , basis function , artificial neural network , mathematics , artificial intelligence , adaptive filter , medicine , mathematical analysis , biochemistry , eigenvalues and eigenvectors , physics , chemistry , quantum mechanics , gene , radiology
In this article, we propose a novel complex radial basis function network approach for dynamic behavioral modeling of nonlinear power amplifier with memory in 3 G systems. The proposed approach utilizes the complex QR‐decomposition based recursive least squares (QRD‐RLS) algorithm, which is implemented using the complex Givens rotations, to update the weighting matrix of the complex radial basis function (RBF) network. Comparisons with standard least squares algorithms, in batch and recursive process, the QRD‐RLS algorithm has the characteristics of good numerical robustness and regular structure, and can significantly improve the complex RBF network modeling accuracy. In this approach, only the signal's complex envelope is used for the model training and validation. The model has been validated using ADS simulated and real measured data. Finally, parallel implementation of the resulting method is briefly discussed. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.