PROJECT TITLE:

Nonlinear modeling of streaming electrification measured in swinging cylinder system

ABSTRACT:

This paper is related to the phenomenon of streaming electrification. The selected results of nonlinear identification of the electrification process are presented. We tend to described the signals used for identification and validation and then 2 structures of the models used: the nonlinear autoregressive model and the Hammerstein-Wiener model. We tend to then conducted parametric identification and compared the obtained results. We tend to used the Gauss operate because the input signal. In the case of the autoregressive model, we used the sigmoid network to estimate the nonlinearity. In the case of the Hammerstein-Wiener model, we tend to tested varied types of input nonlinearities. The piecewise linear nonlinearity estimators turned out to be the most effective. Results for the foremost effective models of both structures: the results of the nonlinear ARX model and the Hammerstein-Wiener model were similar and had varying match values within the vary of 1 to three %. They also had similar generalisation options. In all cases, we have a tendency to also obtained similar shapes of the processes of residues and correlation functions. They indicate a relatively faultless illustration of the responses of models as well as sure variations, especially in the tip half of the processes representing the given method.