Abstract
The analysis of experimental measurements is sometimes difficult, if the registered data are superimposed be noisy signals. The source of such noise is often the improper sensitivity calibration of measuring devices. Sometimes, such data are even useless for the further analysis. Therefore, the goal of the present paper is an attempt of application of two different filtering techniques, including the artificial intelligence methods (artificial neural networks) to the filtering of the noisy experimental data. Examples of filtering results using described techniques are presented.