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A decomposition approach to type 2 interval arithmetic

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Języki publikacji
EN
Abstrakty
EN
The classic interval has precise borders A = [a, ā] . Therefore, it can be called a type 1 interval. Because of great practical importance of such interval data, several versions of type 1 interval arithmetic have been created. However, sometimes precise borders a and ā of intervals cannot be determined in practice. If the borders are uncertain, then we have to do with type 2 intervals. A type 2 interval can be denoted as AT2 = [aL, aR], [āL, āR]. The paper presents multidimensional decomposition RDM type 2 interval arithmetic (D-RDM-T2-I arithmetic), where RDM means relative-distance measure. The decomposition approach considerably simplifies calculations and is transparent for users. Apart from this arithmetic, examples of its applications are also presented. To the authors’ best knowledge, no papers on this arithmetic exist. D-RDM-T2-I arithmetic is necessary to create type 2 fuzzy arithmetic based on horizontal µ-cuts, which the authors aim to do.
Rocznik
Strony
185--201
Opis fizyczny
Bibliogr. 23 poz., rys., tab., wykr.
Twórcy
  • Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland
  • Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, ul. Żołnierska 49, 71-210 Szczecin, Poland
Bibliografia
  • [1] Abolmasoumi, S. and Alavi, M. (2014). A method for calculating interval linear system, Journal of Mathematics and Computer Science 8(3): 193–204.
  • [2] Allahviranloo, T. and Babakordi, F. (2017). Algebraic solution of fuzzy linear system as: AX + BX = Y, Soft Computing 21(24): 7463–7472.
  • [3] De Figueiredo, L.H. and Stolphi, J. (2004). Affine arithmetic: Concepts and applications, Numerical Algorithms 37(1–4): 147–158.
  • [4] Dymowa, L. (2011). Soft Computing in Economics and Finance, Springer, Berlin/Heidelberg.
  • [5] Kaucher, E. (1980). Interval analysis in the extended interval space IR, in G. Alefeld and R.O. Grigorieff (Eds), Fundamentals of Numerical Computation (Computer-Oriented Numerical Analysis), Springer, Vienna, pp. 33–49.
  • [6] Lala, Z.M. (2017). Application of RDM interval arithmetic in decision making problem under uncertainty, Procedia Computer Science 120: 788–796.
  • [7] Landowski, M. (2015). Differences between Moore and RDM interval arithmetic, in P. Angelov et al. (Eds), Intelligent Systems’2014, Springer, Heidelberg/New York, NY, pp. 331–340.
  • [8] Lodwick, W.A. (1999). Constrained interval arithmetic, CCM report, University of Colorado at Denver, Denver, CO, http://www-math.ucdenver.edu/ccm/reports/index.shtml.
  • [9] Lodwick, W.A. and Dubois, D. (2015). Interval linear systems as a necessary step in fuzzy linear systems, Fuzzy Sets and Systems 281(15): 227–251.
  • [10] Mazandarani, M., Pariz, N. and Kamyad, A.V. (2018). Granular differentiability of fuzzy-number-valued functions, IEEE Transactions on Fuzzy Systems 26(1): 310–323.
  • [11] Moore, R. (1966). Interval Analysis, Prentice-Hall, Englewood Cliff, NJ.
  • [12] Najariyan, M. and Zhao, Y. (2017). Fuzzy fractional quadratic regulator problem under granular fuzzy fractional derivatives, IEEE Transactions on Fuzzy Systems 26(4): 2273–2288.
  • [13] Piegat, A. and Landowski, M. (2013). Two interpretations of multidimensional RDM interval arithmetic: Multiplication and division, International Journal of Fuzzy Systems 15(4): 486–496.
  • [14] Piegat, A. and Landowski, M. (2015). Horizontal membership function and examples of its applications, International Journal of Fuzzy Systems 17(1): 22–30.
  • [15] Piegat, A. and Landowski, M. (2017). Is an interval the right result of arithmetic operations on intervals?, International Journal of Applied Mathematics and Computer Science 27(3): 575–590, DOI: 10.1515/amcs-2017-0041.
  • [16] Piegat, A. and Landowski, M. (2018). Solving different practical granular problems under the same system of equations, Granular Computing 3(1): 39–48.
  • [17] Piegat, A. and Pluciński, M. (2015). Fuzzy number addition with the application of horizontal membership functions, Scientific World Journal 2015, Article ID: 367214, DOI: 10.1155/2015/367214.
  • [18] Piegat, A. and Pluciński, M. (2017). Fuzzy number division and the multi-granularity phenomenon, Bulletin of the Polish Academy of Sciences: Technical Sciences 65(4): 497–511.
  • [19] Pluciński, M. (2015). Solving Zadeh’s challenge problems with the application of RDM-arithmetic, International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, pp. 239–248.
  • [20] Sharghi, P., Jabbarova, K. and Aliyeva, K. (2017). RDMinterval arithmetic based decision making on port selection, Procedia Computer Science 120: 572–579.
  • [21] Stolphi, J. and De Figueiredo, L. (2003). An introduction to affine arithmetic, Trends in Applied and Computational Mathematics 4(3): 297–312.
  • [22] Sunaga, T. (2009). Theory of an interval algebra and its application to numerical analysis, Japan Journal of Industrial and Applied Mathematics 26(2–3): 125–143.
  • [23] Warmus, M. (1956). Calculus of approximations, Bulletin de l’Academie Polonaise de Sciences 4(5): 253–257.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f7a752d1-a688-43b0-a0b6-126f889feeba
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