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The adaptive neuro-fuzzy inference system (ANFIS) has been widely used for modeling different kinds of nonlinear systems including RF power amplifiers (PAs). The modified ANFIS (MANFIS) architecture is simpler than that of ANFIS, but with nearly the same performance for modeling nonlinear systems. In this paper, the MANFIS is applied to model RF PAs with memory effects. The simulation and experimental results both in the time and frequency domains show that this model has good modeling accuracy and the characteristics of faster convergence and lower computational complexity compared with the ANFIS model. The normalized mean squared errors of the MANFIS model are slightly lower than those of some other neural network models such as the real-valued time delay neural network, radial basis-function neural network, etc. Finally, the MANFIS model is successfully used in a digital predistortion system, which can provide over 10- dB adjacent channel leakage ratio improvement for three-carrier wideband code division multiple access signals.