Title :
ANN modeling of synthetic cold loads
Author :
Langoni, Diego ; Weatherspoon, Mark H.
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida A&M Univ., Tallahassee, FL, USA
Abstract :
Preliminary results are presented for artificial neural network (ANN) models of the available output noise temperature of a FET-based synthetic cold load. Two different ANNs were studied for this application: the radial basis function (RBF) and the Levenberg-Marquardt (LM) backpropagation (BP). The best average relative error (ARE) and maximum local relative error (MLRE) results for the model of incident noise temperature versus load impedance were 0.1439% and 1.1544% respectively. The best ARE and MLRE results for the model of incident noise temperature versus load reflection coefficient were 0.1810% and 1.5044% respectively.
Keywords :
Schottky gate field effect transistors; backpropagation; load (electric); radial basis function networks; ANN modeling; FET-based synthetic cold load; Levenberg-Marquardt backpropagation; artificial neural network models; average relative error; maximum local relative error; output noise temperature; radial basis function; synthetic cold loads; Calibration; Error analysis; Impedance measurement; Measurement standards; Numerical simulation; RF signals; Radio frequency; Reflection; Sensitivity analysis; Testing; Artificial neural networks (ANNs); Levenberg-Marquardt backpropagation; noise temperature; radial basis function (RBF); synthetic cold load;
Conference_Titel :
ARFTG Conference, 2007 69th
Conference_Location :
Honolulu, HI
Print_ISBN :
978-0-7803-9762-0
Electronic_ISBN :
978-0-7803-9763-7
DOI :
10.1109/ARFTG.2007.5456338
