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A realistic tolerant solution of a system of interval linear equations with the use of multidimensional interval arithmetic

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Języki publikacji
EN
Abstrakty
EN
The paper presents a method of determining the robustness of solutions of systems of interval linear equations (ILEs). The method can be applied also for the ILE systems for which it has been impossible to find solutions so far or for which solutions in the form of improper intervals have been obtained (which cannot be implemented in practice). The research conducted by the authors has shown that for many problems it is impossible to arrive at ideal solutions that would be fully robust to data uncertainty. However, partially robust solutions can be obtained, and those with the highest robustness can be selected and put into practice. The paper shows that the degree of robustness to the uncertainty of the entire system can be calculated on the basis of the degrees of robustness of individual equations, which greatly simplifies calculations. The presented method is illustrated with a series of examples (also benchmark ones) that facilitate its understanding. It is an extension of the authors’ previously published method for first-order ILEs.
Rocznik
Strony
229--247
Opis fizyczny
Bibliogr. 28 poz., rys., tab., wykr.
Twórcy
  • Faculty of Computer Science and Information Systems, West Pomeranian University of Technology, Żołnierska 49, 71-210 Szczecin, Poland
  • Faculty of Computer Science and Information Systems, West Pomeranian University of Technology, Żołnierska 49, 71-210 Szczecin, Poland
Bibliografia
  • [1] Alamanda, S. and Boddeti, K. (2021). Relative distance measure arithmetic-based available transfer capability calculation with uncertainty in wind power generation, International Transactions on Electrical Energy Systems 31(11): e13112.
  • [2] Barth, W. and Nuding, E. (1974). Optimale lösung von intervallgleichungssystemen, Computing 12(2): 117-125.
  • [3] Boukezzoula, R., Foulloy, L., Coquin, D. and Galichet, S. (2019). Gradual interval arithmetic and fuzzy interval arithmetic, Granular Computing 6(2): 451-471.
  • [4] Boukezzoula, R., Galichet, S., Foulloy, L. and Elmasry, M. (2014). Extended gradual interval (EGI) arithmetic and its application to gradual weighted averages, Fuzzy Sets and Systems 257: 67-84.
  • [5] Dubois, D. and Prade, H. (2008). Gradual elements in a fuzzy set, Soft Computing 12: 165-175.
  • [6] Dymova, L. (2011). Soft Computing in Economics and Finance, Springer Verlag, Berlin/Heidelberg.
  • [7] Gay, D. (1982). Solving linear interval equations, SIAM Journal on Numerical Analysis 19(4): 858-870.
  • [8] Kaczorek, T. and Ruszewski, A. (2022). Global stability of discrete-time feedback nonlinear systems with descriptor positive linear parts and interval state matrices, International Journal of Applied Mathematics and Computer Science 32(1): 5-10, DOI: 10.34768/amcs-2022-0001.
  • [9] Kreinovich, V. (2016). Solving equations (and systems of equations) under uncertainty: How different practical problems lead to different mathematical and computational formulations, Granular Computing 1(3): 171-179.
  • [10] Lodwick, W. and Dubois, D. (2015). Interval linear systems as a necessary step in fuzzy linear systems, Fuzzy Sets and Systems 281: 227-251.
  • [11] Lodwick, W. and Thipwiwatpotjana, P. (2017). Flexible and Generalized Uncertainty Optimization: Theory and Methods, Studies in Computational Intelligence, Vol. 696, Springer, Berlin/Heidelberg.
  • [12] Fortin, J., Dubois, D. and Fargier, H. (2008). Gradual numbers and their application to fuzzy interval analysis, IEEE Transactions on Fuzzy Systems 16(2): 388-402.
  • [13] Mazandarani, M., Pariz, N. and Kamyad, A. (2018). Granular differentiability of fuzzy-number-valued functions, IEEE Transactions on Fuzzy Systems 26(1): 310-323.
  • [14] Ngo, V. and Wu, W. (2021). Interval distribution power flow with relative-distance-measure arithmetic, IEEE Transactions on Smart Grid 12(5): 3858-3867.
  • [15] Piegat, A. and Dobryakova, L. (2020). A decomposition approach to type 2 interval arithmetic, International Journal of Applied Mathematics and Computer Science 30(1): 185-201, DOI: 10.34768/amcs-2020-0015.
  • [16] Piegat, A. and Landowski, M. (2012). Is the conventional interval-arithmetic correct?, Journal of Theoretical and Applied Computer Science 6(2): 27-44.
  • [17] 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.
  • [18] Piegat, A. and Pluciński, M. (2015a). 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, DOI: 10.1515/amcs-2015-0049.
  • [19] Piegat, A. and Pluciński, M. (2015b). Fuzzy number addition with the application of horizontal membership functions, The Scientific World Journal 2015, Article ID: 367214.
  • [20] 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.
  • [21] Piegat, A. and Pluciński, M. (2022a). The optimal tolerance solutions of the basic linear equation and the explanation of the Lodwick’s anomaly, Applied Sciences 12(4): 4382.
  • [22] Piegat, A. and Pluciński, M. (2022b). Realistic optimal tolerant solution of the quadratic interval equation and determining the optimal control decision on the example of plant fertilization, Applied Sciences 12(21): 10725.
  • [23] Shary, S. (1991). Optimal solution of interval linear algebraic systems, Interval Computations 2: 7-30.
  • [24] Shary, S. (1992). On controlled solution set of interval algebraic systems, Interval Computations 6(6): 66-75.
  • [25] Shary, S. (1994). Solving the tolerance problem for interval linear systems, Interval Computations 2: 6-26.
  • [26] Shary, S. (1995). Solving the linear interval tolerance problem, Mathematics and Computers in Simulation 39(1-2): 53-85.
  • [27] Siahlooei, E. and Shahzadeh Fazeli, S. (2018). Two iterative methods for solving linear interval systems, Applied Computational Intelligence and Soft Computing 2018, Article ID: 2797038.
  • [28] Zadeh, L. (1975). The concept of a linguistic variable and its application to approximate reasoning-I, Information Sciences 8(3): 199-249.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cd4dcc64-c748-4ead-a3c1-658ce5f061da
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