Abstract
This paper develops and assesses multiple optimized Machine Learning (ML) based techniques to discover the appropriateness of them in modelling small-signal behaviors of Gallium Nitride High Electron Mobility Transistors (GaN HEMTs). At first, ML techniques namely Gaussian Process Regression (GPR), Generalized Regression Neural Networks (GRNNs), Radial Basis Neural Networks (RBNNs), and Artificial Neural Networks (ANNs) based behavioral models for GaN HEMTs are developed. Then, the developed models' accuracy and efficiency are further enhanced using rigorous hyperparameters optimization. At last, the optimized models are compared using conventional metrics namely mean squared error, mean absolute error, coefficient of regression (R2), time required to train the models, and the ability to imitate the behavior at biasing conditions. We found GPR-. GRNNs-, RBNNs- and ANN-based models provided average$\%\mathrm{R}^{2} =99.77, 99.44, 99.22\text{and }99.86$, and required training time = 337.8 (s), 21.04 (s), 683.47 (s) and 17.8 (s), respectively.