Aiming at the nonlinear modeling of Power Amplifier (PA), an improved Radial Basis Function Neural Networks (RBFNN) model was proposed. Firstly, time-delay of cross terms and output feedback were added in the input. Parameters (weigths and centers) of the proposed model were extracted using the Orthogonal Least Square (OLS) algorithm. Then Doherty PA was trained and validated successfully by 15MHz three-carrier Wideband Code Division Multiple Access (WCDMA) signal, and the Normalized Mean Square Error (NMSE) can reach -45dB. Finally, the inverse class F power amplifier was used to test the universality of the model. The simulation results show that the model can more truly fit characteristics of power amplifier.