Hyperparameter Optimization of Two-Hidden-Layer Neural Networks for Power Amplifiers Behavioral Modeling Using Genetic Algorithms

Abstract

Neural networks (NNs) are efficient techniques for behavioral modeling of power amplifiers (PAs). This letter proposes a genetic algorithm to determine the optimal hyperparameters of the NN model for a PA. Different activation functions are compared. The necessary number of training epochs is also studied to get an optimal solution with a significantly reduced computational complexity. Experimental measurements on a PA with different signals validate the NN models determined by the proposed method.