Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers
This work addresses power-supply induced intermodulation distortion in Class-D Power Amplifiers, offering a more efficient modeling approach for engineers in electronics and signal processing, though it appears incremental as it builds on existing neural network methods.
The paper tackled modeling nonlinear distortion in Class-D Power Amplifiers by proposing an Elman Wavelet Neural Network (EWNN) model, which achieved a square sum error of 10^-3 in 31 iteration steps compared to 86 steps for a basic Elman network and outperformed a Volterra-Laguerre model in accuracy with fewer parameters.
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) $ε_{min}=10^{-3}$, 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.