Estimation of Kautz Poles in Wiener-Volterra Models Using Levenberg-Marquardt Algorithm

Abstract

This work approaches the problem of estimating the Kautz optimal poles in kernel expansion in Wiener-Volterra models. The analytical solution for the suboptimal case is already established in the literature. However, the solution for the two parameters that compose the poles is still open. In this paper, an optimization strategy using the Levenberg-Marquardt is presented. This algorithm is used to find kernel expansion parameters, with the same base for all dimensions. The construction of bases using digital filter is considered. To validate the implemented algorithm, data collected from the excitation of an electrically coupled drive system was used to analyze the impact of the search space thresholds and the behavior of Levenberg-Marquardt’s parameters. It was also analyzed the impact on the model accuracy, as the number of functions in the base is increased. As a result, the models determined have achieved better results than the works found in the literature.