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) emin = 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.