Novel neural network optimization approach for modeling scattering and noise parameters of microwave transistor

Abstract

AbstractThis study performed modeling of the scattering (S) and noise (N) parameters of the ATF53189 using the General Regression Neural Network (GRNN) and Multi Layer Perceptron Neural Network (MLPNN) methods based on Artificial Neural Network (ANN). For modeling the linear behavior of the transistor, the optimum design parameters of the GRNN and the MLPNN methods were determined using four different optimization algorithms. These are whale optimization algorithm (WOA), artificial bee colony (ABC), particle swarm optimization (PSO) and ant lion optimizer (ALO) algorithms. With the help of these algorithms, the sigma parameter of the GRNN and the number of hidden layers, numbers of neurons in the hidden layers and the activation functions of the hidden layers of the MLPNN were optimized. This way, the best models required for prediction of the S and N parameters of the ATF53189 were obtained. Different models that provided each of the angles and magnitudes of the S11, S21, S12, S22 parameters and the Fmin, Γmin, Γopt magnitude, Γopt angle and Rn noise parameters as the output were created. The experimental results showed that the GRNN method should be used in linear behavior modeling of S parameters of the ATF53189 and the MLPNN method should be used in linear behavior modeling of N parameters of the ATF53189. It is understood that the best algorithm for optimizing the design parameters of the GRNN and the MLPNN methods is the PSO. As a result, the modeling of the S and N parameters of the ATF53189 transistor was successfully carried out with the methods used in this study.