Empirical White Noise Processes and the Subjective Probabilistic Approaches

  • András Rövid Department of Automotive Technologies, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, H-1111 Budapest, Stoczek street 6, Hungary
  • László Palkovics Department of Automotive Technologies, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, H-1111 Budapest, Stoczek street 6, Hungary; Research Center of Vehicle Engineering, Széchenyi István University, H-9026 Győr, Egyetem tér 1., Hungary
  • Péter Várlaki Department of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, H-1111, Budapest, Stoczek street 2, Hungary; Research Center of Vehicle Engineering, Széchenyi István University, H-9026 Győr, Egyetem tér 1., Hungary

Abstract

The paper discusses the identification of the empirical white noise processes generated by deterministic numerical algorithms.
The introduced fuzzy-random complementary approach can identify the inner hidden correlational patterns of the empirical white noise process if the process has a real hidden structure of this kind. We have shown how the characteristics of auto-correlated white noise processes change as the order of autocorrelation increases. Although in this paper we rely on random number generators to get approximate white noise processes, in our upcoming research we are planning to turn the focus on physical white noise processes in order to validate our hypothesis.

Keywords: empirical white noise processes, system identification, fuzzy-random view and possibility distributions, random number generators, nth order autocorrelation
Published online
2019-10-31
How to Cite
Rövid, A., Palkovics, L. and Várlaki, P. (2020) “Empirical White Noise Processes and the Subjective Probabilistic Approaches”, Periodica Polytechnica Transportation Engineering, 48(1), pp. 19-30. doi: https://doi.org/10.3311/PPtr.15165.
Section
Articles