Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers

TL;DR

This paper introduces an Elman Wavelet Neural Network (EWNN) model to accurately simulate nonlinear distortion in Class-D Power Amplifiers, demonstrating faster convergence and fewer parameters than traditional models.

Contribution

The paper proposes a novel EWNN model using Morlet wavelet functions for improved accuracy and efficiency in modeling CDPA nonlinearities compared to existing models.

Findings

EWNN achieves the same error with fewer iterations than BENN.

EWNN requires significantly fewer parameters than Volterra-Laguerre models.

EWNN demonstrates higher accuracy and faster convergence in simulations.

Abstract

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.