This study presents an improved large signal model that can be used for High Electron Mobility Transistors (HEMTs) and Field Effect Transistors (FETs) using measurement-based behavioral modeling techniques. The steps for accurate large and small signal modeling for transistor are also discussed. The proposed DC model is based on the Fager model since it compensates between the number of model's parameters and accuracy. The objective is to increase the accuracy of the drain-source current model with respect to any change in gate-source or drain-source voltages. Also, the objective of this thesis work is to extend the improved DC model to account for soft breakdown and kink effect found in some variants of HEMT devices. A hybrid Newtons-Genetic algorithm is used in order to determine the unknown parameters in the developed model. In addition to accurate modeling of a transistor's DC characteristics, the complete large signal model is modeled using behavioral modeling techniques based on multi-bias s-parameter measurements. The targeted elements to be modeled in the complete large signal model are parasitic capacitances, parasitic inductances and parasitic resistances. The way that the complete model is performed is by using a hybrid multi-objective optimization technique (Non Dominated Sorting Genetic Algorithm II) and local minimum search (multi-variable Newton's method). Finally, the results of DC modeling and multi-bias s-parameters modeling are presented, and three device modeling recommendations are discussed.