Cost‐effective GRNN‐based modeling of microwave transistors with a reduced number of measurements

Abstract

In this article, a simple, accurate, fast, and reliable black-box modeling is proposed for the scattering (S)-parameters and noise (N)-parameters of microwave transistors using the general regression neural network (GRNN) with the substantially reduced measurements and computational cost. In this modeling method, GRNN is employed as a nonlinear extrapolator to generalize the S-data and N-data belonging to only a single bias voltage in the middle region into the entire device operation domain of the bias condition (V DS /V CE , I DS /I C , f) within the shortened human effort. The proposed method is implemented to the modeling of the two transistors BFP640 and ATF-551 M4 as study cases. Thus, comparisons are made with the multilayer perceptrons, trained by the two standard backward propagation algorithms, which are the Levenberg-Marquardt, Bayesian regularization and the 10 data mining methods recently published in the literature using the chosen training data sets in both ınterpolation and extrapolation types of generalization. All the comparisons are achieved using four criteria commonly used in the literature. It can be concluded that GRNN is found to be a fast and accurate modeling method that extrapolates the reduced amount of training data consisting of measured S-parameters and N-parameters at the typical currents of the middle bias voltage to the wide operating range.

Signal and Noise Modeling of Microwave Transistors Using Characteristic Support Vector-based Sparse Regression

“…In this work first time in the literature, SVRMs are employed successfully as nonlinear extrapolators in the black-box modeling of Scattering and Noise parameters of a microwave transistor as counterparts to the Generalized Regression Neural Network GRNNs [8][9][10]. Thus this work can be considered mainly in the following significant contribution in the transistor modeling: Magnitude and phase of each characterization S-or N-parameter can be expressed as a continuous function in the throughout device operation domain of (V DS , I DS , f) using only a subset of the reduced training data so-called Characteristic SVs.…”

“…We must find  Lagrangian multipliers Furthermore error-metric analysis of both of the two transistors will also be given as compared to the counterpart method GRNN which has also been worked out for the transistor modeling by our research group in [8][9][10].…”

“…In recent years, regression with Support Vector Machine (SVM)s and probability based neural networks named as Generalized Regression Neural Network (GRNN) remove these handicaps and facilitate to substantially reduce the expensive fine discrete training data with their typical modeling applications to transistor [7][8][9][10], integrated microstrip and slot antennas in [11], [12], printed transmission lines [13][14][15] and vertical interconnects in microwave packaging structures [16].…”

Design optimization of a pattern reconfigurable microstrip antenna using differential evolution and 3D EM simulation‐based neural network model

“…ANN provides an efficient solution for design optimization problems where only the input‐output data are given to the ANN to create a mapping from the input to the output data in order to predict fast and accurate results based on simulated or measured data. In References , the scattering parameters of a microwave transistor by using ANN based models created from either simulated or measured data provided by transistor manufactures. In References , the reflection phase of a unit reflect array antenna are proposed where with the usage of ANN model the total design optimization process is greatly reduced.…”

Application of artificial intelligence algorithms on modeling of reflection phase characteristics of a nonuniform reflectarray element

“…For the last decade, the ANN models have been used for a variety of regression problems in microwave circuit design such as small and large signal modeling of active devices, 29 temperature-based models of microwave transistors, 30 modeling of microstrip line, 31 modeling of Minkowski RA, 32,33 and regression of scattering parameters of a microwave transistor with respect to its direct current (DC) bias conditions. 34 Recently, GP or SR is being used for modeling of alternating current (AC)/DC rectifiers for radio frequency identification (RFID) applications, 35 derivation of an accurate analytical formula for characteristic impedance calculation of a microstrip transmission line, 36 and prediction of reflection phase characteristics of a 3D printable nonuniform RA (NURA). 37 Also, it should be noted that there are studies on machine learning algorithms proposed to solve nonlinear problems such as characterization of microstrip line models for ultra-wideband low-noise amplifier designs, 38 design optimization of a front-end amplifier, 39 signal and noise modeling of microwave transistors, 40 prediction of brain maturity in infants, 41 modeling the resonant frequency of microstrip antenna, 42 and estimation of direction of arrival.…”