Data filtering using dynamic particles method

The identification of the industrial processes is a complex problem, especially in the case of signals denoising. The holistic approaches used for signal denoising processes are recently considered in various types of applications in the domain of experimental simulations, feature extraction and identification. A new signal filtering method based on the dynamic particles (DP) approach is presented. It employs physics principles for the signal smoothing. The presented method was validated in the identification of two kinds of input data sets: artificially generated data according to a given function y = f (X) and the data obtained in laboratory mechanical tests of metals. The algorithm of the DP method and the results of calculations are presented. The obtained results were compared with commonly used denoising techniques including weighted average, neural networks and wavelet analysis. Moreover the assessment of the results' quality is introduced.