Dynamic Behavioral Modeling of Power Amplifiers Using ANFIS-Based Hammerstein

This letter presents a novel structure for the dynamic behavioral modeling of radio frequency power amplifiers (RF PAs) with ANFIS-based Hammerstein model for memory effects. The model is an adaptive neuro-fuzzy inference system (ANFIS) followed by a finite impulse response (FIR) filter. A hybrid learning algorithm is adopted to identify the parameters of the ANFIS. The parameters of the FIR filter are estimated by a straightforward least-squares method. The input and output signals of the PA excited with a three-carrier WCDMA signal were sampled for the model identification and validation in a test bench. Experimental results in the frequency and the time domains show that the proposed model was able to give an accurate approximation to characterize the wideband RF PAs.