  In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate
with the input signal, manifesting into power-supply induced intermodulation
distortion (PS-IMD) and due to the memory effects of the system, there exist
asymmetries in the PS-IMDs. In this paper, a new behavioral modeling based on
the Elman Wavelet Neural Network (EWNN) is proposed to study the nonlinear
distortion of the CDPAs. In EWNN model, the Morlet wavelet functions are
employed as the activation function and there is a normalized operation in the
hidden layer, the modification of the scale factor and translation factor in
the wavelet functions are ignored to avoid the fluctuations of the error
curves. When there are 30 neurons in the hidden layer, to achieve the same
square sum error (SSE) $\epsilon_{min}=10^{-3}$, EWNN needs 31 iteration steps,
while the basic Elman neural network (BENN) model needs 86 steps. The
Volterra-Laguerre model has 605 parameters to be estimated but still can't
achieve the same magnitude accuracy of EWNN. Simulation results show that the
proposed approach of EWNN model has fewer parameters and higher accuracy than
the Volterra-Laguerre model and its convergence rate is much faster than the
BENN model.
