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Neural networks for large- and small-signal modeling of MESFET/HEMT transistors

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3 Author(s)
M. Lazaro ; DICOM, Cantabria Univ., Santander, Spain ; I. Santamaria ; C. Pantaleon

In this paper, we present a comparative study of three neural networks-based solutions for large- and small-signal modeling of MESFET and HEMT transistors. The first two neural architectures are specific for this modeling problem: the generalized radial basis function (GRBF) network, and the smoothed piecewise linear (SPWL) model. These models are compared with the well-known multilayer perceptron (MLP) network. Results are presented for both the large- and small-signal regimes separately. Finally, a global model is proposed that is able to accurately characterize the whole behavior of the transistors. This model is based on a simple combination of the best models obtained for the two kinds of regimes

Published in:

IEEE Transactions on Instrumentation and Measurement  (Volume:50 ,  Issue: 6 )