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Position fixing and uncertainty

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Języki publikacji
EN
Abstrakty
EN
Taken random observations are usually accompanied by rectified knowledge regarding their behaviour. In modern computer applications, raw data sets are usually exploited at learning phase. At this stage, available data are explored in order to extract necessary parameters required within the inference scheme computations. Crude data processing enables conditional dependencies extraction. It starts with upgrading histograms and their uncertainty estimation. Exploiting principles of fuzzy systems one can obtain modified step-wise structure in the form of locally injective density functions. They can be perceived as conditional dependency diagrams with identified uncertainty that enables constructing basic probability assignments. Belief, uncertainty and plausibility measures are extracted from initial raw data sets. The paper undertakes problem of belief structures upgraded from uncertainty model in order to solve the position fixing problem. The author intention is presenting position fixing as an inference scheme. The scheme engages evidence, hypothesis and revokes concept of conditional relationships.
Twórcy
  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
  • [1] Cuzzolin, F. The Geometry of Uncertainty: The Geometry of Imprecise Probabilities (Artificial Intelligence: Foundations, Theory, and Algorithms). Springer, 2021. ISBN-10: 3030631524.
  • [2] Dempster A.P A Generalization of Bayesian Inference. Springer Berlin Heidelberg, 2008, pp. 73–104.
  • [3] Denoeux, T. Allowing Imprecision in Belief Representation Using Fuzzy-valued Belief Structures. Proceedings of Information Processing and Management of Uncertainty, Paris, 1998, pp. 28–55.
  • [4] Filipowicz W. Mathematical Theory of Evidence in Navigation. In Belief Functions: Theory and Applications, Third International Conference, BELIEF Oxford, Cuzzolin F. (ed), Springer International Publishing Switzerland, 2014, pp. 199–208.
  • [5] Filipowicz W. A logical device for processing nautical data. Scientific Journals of the Maritime University of Szczecin, Volume 52(124), 2017, pp. 65-73.
  • [6] Filipowicz W. Imprecise data handling with MTE. InProceedings of 11th International Conference on Computational Collective Intelligence, B. Trawinski (ed), Hendaye, France, 2019.
  • 7] Filipowicz W. Conditional dependencies in imprecise data handling, In Proceedings of 25th International Conference on Knowledge-Based and Intelligent Information & Engineering. Procedia Computer Science 192, 2021, pp. 80–89.
  • [8] Filipowicz Wł., Conditional Dependencies and Position Fixing. Appl. Sci. (ISSN 2076-3417) 2022, 12, 12324. DOI: 10.3390/app1223123241.
  • [9] Hau H.Y., Kashyap R.L., Belief combination and propagation in a lattice-structured inference network. IEEE Transactions on Systems, Man and Cybernetics vol. 20, s. 45-57, 1990 DOI: 10.1109/21.47808 10] Lee E. S., Zhu Q. Fuzzy and Evidence Reasoning, Physica-Verlag, Heidelberg, 1995.
  • [11] Shafer G. A Mathematical Theory of Evidence, Princeton University Press, 1976.
  • [12] Yager R. R., On the normalization of fuzzy belief structure. International Journal of Approximate Reasoning, vol. 14, 1996, s. 127-153.
  • [13] Yen J. Generalizing the Dempster – Shafer theory to fuzzy sets. IEEE Transactions on Systems, Man and Cybernetics, 1990, Volume 20(3).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7621b28f-4382-4f4b-bfa4-8d050f70486a
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