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Tytuł artykułu

Fuzzy number division and the multi-granularity phenomenon

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents difficulties connected with fuzzy and interval division. If operations such as fuzzy addition, subtraction and multiplication provide as a result one compact, multidimensional granule, then a result of the fuzzy division can consists of few separated granules. Such results are more difficult to use in next calculations. The paper shows that the number of solution granules can be higher than 2 and that in certain problems division does not occur explicitly. In certain problems, separation of particular solution granules can be considerable. The paper also shows how to realize the fuzzy division when its denominator contains zero. Most types of fuzzy arithmetics forbid such operation. However, the paper shows that it is possible. Multidimensional fuzzy RDM arithmetic and horizontal membership functions which facilitate detecting of solution granules are also described. The considered problems are visualized by examples.
Rocznik
Strony
497--511
Opis fizyczny
Bibliogr. 29 poz., wykr.
Twórcy
autor
  • Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, 49 Żołnierska St., 71-210 Szczecin, Poland
  • Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, 49 Żołnierska St., 71-210 Szczecin, Poland
Bibliografia
  • [1] D. Dubois and H. Prade, “Operations on fuzzy numbers”, International Journal of Systems Science 9 (6), 613–626 (1978).
  • [2] P. Dutta, H. Boruah, and T. Ali, “Fuzzy Arithmetic with and without using α-cut method: A Comparative study”, International Journal of Latest Trends in Computing 2 (1), 99–107 (2011).
  • [3] J. Fodor and B. Bede, “Arithmetics with fuzzy numbers: A comparative overview”, Proc. Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence, 54–68 (2006).
  • [4] W. Pedrycz, A. Skowron, and V. Kreinovich, Handbook of Granular Computing, John Wiley & Sons, Chichester, 2008.
  • [5] A. Piegat and M. Landowski, “Two interpretations of multidimensional RDM interval arithmetic: Multiplication and division”, International Journal of Fuzzy Systems 15 (4), 486–496 (2013).
  • [6] A. Piegat and M. Pluciński, “Fuzzy number addition with the application of horizontal membership functions”, The Scientific World Journal 2015, Article ID 367214 (2015).
  • [7] K. Tomaszewska, “The application of horizontal membership function to fuzzy arithmetic operations”, Journal of Theoretical and Applied Computer Science 8 (2), 3–10 (2014).
  • [8] R.E. Moore, R.B. Kearfott, and M.J. Cloud, Introduction to Interval Analysis, Society for Industrial and Applied Mathematics, Philadelphia, 2009.
  • [9] B. Liu, Uncertainty Theory, Springer Verlag, Berlin, 2010.
  • [10] Q.X. Li and S.F. Liu, “The foundation of the grey matrix and the grey input-output analysis”, Applied Mathematical Modelling 32 (3), 267–291 (2008).
  • [11] A. Piegat and M. Pluciński, “Computing with words with the use of inverse RDM models of membership functions”, International Journal of Applied Mathematics and Computer Science 25 (3), 675–688 (2015).
  • [12] L.A. Zadeh, “Fuzzy logic = computing with words”, IEEE Transactions on Fuzzy Systems 4 (2), 103–111 (1996).
  • [13] K. Tomaszewska and A. Piegat, “Application of the horizontal membership function to the uncertain displacement calculation of a composite massless rod under a tensile load”, in Soft Computing in Computer and Information Science, pp. 63–72, eds. A. Wiliński, I. El Fray and J. Pejaś, Springer, 2015.
  • [14] J. Smoczek, “Interval arithmetic-based fuzzy discrete-time crane control scheme design”, Bull. Pol. Ac.: Tech 61 (4), 863–870 (2013).
  • [15] A. Niewiadomski and M. Kacprowicz, “Higher order fuzzy logic in controlling selective catalytic reduction systems”, Bull. Pol. Ac.: Tech 62 (4), 743–750 (2014).
  • [16] J. Smoczek, “P1-TS fuzzy scheduling control system design using local pole placement and interval analysis”, Bull. Pol. Ac.: Tech 62 (3), 455–464 (2014).
  • [17] T. Chen, “Remarks on the subtraction and division operations over intuitionistic fuzzy sets and interval-valued fuzzy sets”, International Journal of Fuzzy Systems 9 (3), 169–173 (2007).
  • [18] A. Piegat and M. Landowski, “Aggregation of inconsistent expert opinions with use of horizontal intuitionistic membership functions”, in Novel Developments in Uncertainty Representation and Processing, pp. 215–224, eds. K. Atanassov, O. Castillo, and J. Kacprzyk, Springer, 2016.
  • [19] B. Qing and H. Wong, “Generalized interval-valued fuzzy variable precision rough sets”, International Journal of Fuzzy Systems 16 (4), 554–565 (2014)
  • [20] A. Kaufmann and M.M. Gupta, Introduction to Fuzzy Arithmetic, Van Nostrand Reinhold, New York, 1991.
  • [21] A. Piegat, Fuzzy Modeling and Control, Physica Verlag, Heidelberg, 2001.
  • [22] M. Hanss, Applied Fuzzy Arithmetic, Springer Verlag, Berlin, 2005.
  • [23] M. Hanss, “The transformation method for the simulation and analysis of systems with uncertain parameters”, Fuzzy Sets and Systems 130 (2), 277–289 (2002).
  • [24] G.J. Klir and Y. Pan, “Constrained fuzzy arithmetic: Basic questions and some answers”, Soft Computing 2 (2), 100–108 (1998).
  • [25] A. Piegat and M. Landowski, “Horizontal membership function and examples of its applications”, International Journal of Fuzzy Systems 17 (1), 22–30 (2015).
  • [26] S.P. Shary, “On optimal solution of interval linear equations”, SIAM Journal on Numerical Analysis 32 (2), 610–630 (1995).
  • [27] P. Sevastjanov and L. Dymova, “A new method for solving interval and fuzzy equations: Linear case”, Information Sciences 179 (7), 925–937 (2009).
  • [28] L. Dymova, Soft Computing in Economics and Finance, Springer Verlag, Berlin, 2011.
  • [29] W.W. Leontief, Input-Output Economics, Oxford University Press on Demand, 1986.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-042628c1-67a4-4380-b2a1-2022a1c24a43
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