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Modeling MESFETs and HEMTs intermodulation distortion behavior using a generalized radial basis function network
Author(s) -
García J. A.,
Tazón Puente A.,
Mediavilla Sánchez A.,
Santamaría I.,
Lázaro M.,
Pantaleón C. J.,
Pedro J. C.
Publication year - 1999
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/(sici)1099-047x(199905)9:3<261::aid-mmce10>3.0.co;2-p
Subject(s) - intermodulation , nonlinear system , distortion (music) , radial basis function , amplifier , generalization , electronic engineering , computer science , nonlinear distortion , artificial neural network , multilayer perceptron , volterra series , current source , topology (electrical circuits) , control theory (sociology) , voltage , telecommunications , engineering , mathematics , electrical engineering , physics , artificial intelligence , mathematical analysis , control (management) , bandwidth (computing) , quantum mechanics
This paper proposes a generalized radial basis function (GRBF) network to accurately describe drain to source current nonlinearity for intermodulation distortion (IMD) prediction of MESFETs and HEMTs applications in their saturated region. Trying to analytically reproduce the nonlinearities second and third order Taylor‐series coefficients, responsible for IMD performance in these devices, may result in a quite difficult task. Neural networks were introduced as a robust alternative for microwave modeling, mostly employing the black‐box model type approach of the multilayer perceptron network. The GRBF network we consider is a generalization of the RBF network, which takes advantage of problem dependent information. Allowing different variances for each dimension of input space, the GRBF network makes use of soft nonlinear dependence of the drain to source current derivatives with drain to source voltage for improving accuracy at reduced cost. The network structure and its learning algorithm are presented. Results of its performance are compared to other structures with similar amounts of parameters. Carrier to intermodulation (C/I) predictions validate this approach for precise IMD control versus bias and load in class A amplifiers applications. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 261–276, 1999.