Imprecise Data Handling with MTE

  • Włodzimierz FilipowiczEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11683)


Measurements, indications and forecasts are randomly and very often systematically corrupted. The distribution of stochastic distortions is empirically evaluated; an estimate is usually given by histogram. Empirical distributions available from various sources differ. Discrepancies are due to testing methodology, conditions of experiments etc. Whatever the reason they introduce some kind of doubtfulness. In many modern applications propagation of uncertainty is an important issue. The ability to model and process uncertainty through traditional approaches is rather limited. To propose new solutions, one should start with an alternative approach towards modelling and processing uncertainty. Mathematical Theory of Evidence might be useful provided supporting measures on representing the true value by any location in the neighborhood of observation are available. Theoretical Gaussian density distribution as well as histograms are exploited. In recent paper by the author transformation from probability density to probability distribution with fuzzy sets was presented. In order to obtain most probable forecast various height histogram bins are converted to fuzzy limited ones. Necessary membership functions are proposed. Paper concludes with example calculations results.


Belief functions Uncertainty Empirical data 



This research was supported by The National Centre for Research and Development in Poland under grant on ROUTING research project (MARTERA-1/ROUTING/3/2018) in ERA-NET COFUND MarTERA-1 programme (2018-2021).


  1. 1.
    Filipowicz, W.: On nautical observation errors evaluation. TransNav 9(4), 545–550 (2015)CrossRefGoogle Scholar
  2. 2.
    Filipowicz, W.: A logical device for processing nautical data. Sci. J. SMU Szczecin 52(124), 65–73 (2017)Google Scholar
  3. 3.
    Filipowicz, W.: Mathematical theory of evidence in navigation. In: Cuzzolin, F. (ed.) BELIEF 2014. LNCS (LNAI), vol. 8764, pp. 199–208. Springer, Cham (2014). Scholar
  4. 4.
    Lee, E.S., Zhu, Q.: Fuzzy and Evidence Reasoning. Physica-Verlag, Heidelberg (1995)zbMATHGoogle Scholar
  5. 5.
    Liu, W., Hughes, J.G., McTear, M.F.: Representing heuristic knowledge and propagating beliefs in Dempster-Shafer theory of evidence. In: Federizzi, M., Kacprzyk, J., Yager, R.R. (eds.) Advances in the Dempster-Shafer Theory of Evidence. Willey, New York (1992)Google Scholar
  6. 6.
    Piegat, A.: Fuzzy Modelling and Control. AOW Exit, Warsaw (2003)zbMATHGoogle Scholar
  7. 7.
    Yen, J.: Generalizing the Dempster–Shafer theory to fuzzy sets. IEEE Trans. Syst. Man Cybern. 20(3), 559–570 (1990)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Gdynia Maritime UniversityGdyniaPoland

Personalised recommendations