A speed and accuracy test of backpropagation and RBF neural networks for small-signal models of active devices

Authors:
Diego Langoni;Mark H. Weatherspoon;Simon Y. Foo;Hector A. Martinez
Affiliations:
Electrical and Computer Engineering, Florida A&M University-Florida State University College of Engineering, FL 32310, USA;Electrical and Computer Engineering, Florida A&M University-Florida State University College of Engineering, FL 32310, USA;Electrical and Computer Engineering, Florida A&M University-Florida State University College of Engineering, FL 32310, USA;Electrical and Computer Engineering, Florida A&M University-Florida State University College of Engineering, FL 32310, USA
Venue:
Engineering Applications of Artificial Intelligence
Year:
2006

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Abstract

Backpropagation networks are compared to radial basis function (RBF) networks when it comes to small signal modeling RF/microwave active devices. The modeled device is a 4x50@mm gate width, 0.25@mm gate length gallium arsenide (GaAs) Metal semiconductor field-effect transistor (MESFET). It is the authors' intent to prove that RBF networks provide much better performance than backpropagation when it comes to this type of modeling. First, two separate backpropagation networks are created to determine the best training algorithm in terms of convergence speed. Then, the backpropagation network, using its best training algorithm, is compared to the RBF network in terms of speed and accuracy. Simulation results are presented in tables and figures for better understanding. All tests and simulations for the backpropagation and RBF networks are done using Matlab's Neural Network Toolbox.