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Is the conventional interval arithmetic correct?

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Języki publikacji
EN
Abstrakty
EN
Interval arithmetic as part of interval mathematics and Granular Computing is unusually important for development of science and engineering in connection with necessity of taking into account uncertainty and approximativeness of data occurring in almost all calculations. Interval arithmetic also conditions development of Artificial Intelligence and especially of automatic thinking, Computing with Words, grey systems, fuzzy arithmetic and probabilistic arithmetic. However, the mostly used conventional Moore-arithmetic has evident weak-points. These weak-points are well known, but nonetheless it is further on frequently used. The paper presents basic operations of RDM-arithmetic that does not possess faults of Moore-arithmetic. The RDM-arithmetic is based on multi-dimensional approach, the Moore-arithmetic on one-dimensional approach to interval calculations. The paper also presents a testing method, which allows for clear checking whether results of any interval arithmetic are correct or not. The paper contains many examples and illustrations for better understanding of the RDM-arithmetic. In the paper, because of volume limitations only operations of addition and subtraction are discussed. Operations of multiplication and division of intervals will be presented in next publication. Author of the RDM-arithmetic concept is Andrzej Piegat.
Rocznik
Strony
27--44
Opis fizyczny
Bibliogr. 17 poz., rys.
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autor
autor
Bibliografia
  • [1] Aliev, R., Pedrycz, W., Fazlollahi, B., Huseynov, O., Alizadeh, A., Guirimov, B. (2012). Fuzzy logic-based generalized decision theory with imperfect information, Information Sciences 189, 18–42.
  • [2] Bronstein, I.N., et al. (2004). Modern compendium of mathematics, (in Polish), Wydawnictwo Naukowe PWN, Warszawa, Poland.
  • [3] Dymova, L. (2011). Soft computing in economics and finance, Springer–Verlag, Berlin, Heidelberg.
  • [4] Hanss, M. (2005). Applied fuzzy arithmetic, Springer–Verlag, Berlin, Heidelberg.
  • [5] Jaroszewicz, S., Korzen, M. (2012a). Arithmetic operations on independent random variables: a numerical approach, SIAM Journal of Scientific Computing, vol. 34, No. 4, pp A1241-A1265.
  • [6] Jaroszewicz, S., Korzen, M. (2012b). Pacal: A python package for arithmetic computations with random variables, http://pacal.sourceforge.net/, on line: September 2012.
  • [7] Kaufmann, A., Gupta, M.M. (1991). Introduction to fuzzy arithmetic, Van Nostrand Reinhold, New York.
  • [8] Liu, S., Lin Forrest, J.Y. (2010). Grey systems, theory and applications. Springer, Berlin, Heidelberg.
  • [9] Moore, R.E. (1966). Interval analysis, Prentice Hall, Englewood Cliffs N.J.
  • [10] Moore, R.E., Kearfott, R.B., Cloud. M.J. (2009). Introduction to interval analysis. SIAM, Philadelphia.
  • [11] Pedrycz, W., Skowron A., Kreinovicz, V. (eds) (2008). Handbook of granular computing. Wiley, Chichester, England.
  • [12] Piegat, A. (2001). Fuzzy control and modeling, Springer–Verlag, Heidelberg, New York.
  • [13] Sengupta, A., Pal, T.K. (2009). Fuzzy preference ordering of interval numbers in decision problems. Springer, Berlin, Heidelberg.
  • [14] Sevastjanov, P., Dymova, L. (2009). A new method for solving interval and fuzzy equations: linear case, Information Sciences 17, 925–937.
  • [15] Sevastjanov, P., Dymova, L., Bartosiewicz, P. (2012). A framework for rule-base evidential reasoning in the interval settings applied to diagnosing type 2 diabets, Expert Systems with Applications 39, 4190–4200.
  • [16] Williamson, R. (1989). Probabilistic arithmetic, Ph.D. thesis, Department of Electrical Engineering, University of Queensland.
  • [17] Zadeh, L.A. (2002). From computing with numbers to computing with words – from manipulation of measurements to manipulation of perceptions. International Journal of Applied Mathematics and Computer Science, Vol.12, No.3, 307–324.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS3-0025-0119
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