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Tytuł artykułu

Towards a linguistic description of dependencies in data

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The problem of a linguistic description of dependencies in data by a set of rules Rk: "If X is Tk then Y is Sk" is considered, where Tk's are linguistic terms like SMALL, BETWEEN 5 AND 7 describing some fuzzy intervals Ak. Sk's are linguistic terms like DECREASING and QUICKLY INCREASING describing the slopes pk of linear functions yk=pkx +qk approximating data on Ak. The decision of this problem is obtained as a result of a fuzzy partition of the domain X on fuzzy intervals Ak, approximation of given data {xi,yi}, i=1,...,n by linear functions yk=pkx+qk on these intervals and by re-translation of the obtained results into linguistic form. The properties of the genetic algorithm used for construction of the optimal partition and several methods of data re-translation are described. The methods are illustrated by examples, and potential applications of the proposed methods are discussed.
Rocznik
Strony
391--401
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
autor
  • Institute of Problems of Informatics, Academy of Sciences of Tatarstan and Kazan State Technological University, K. Marx Str. 68, Kazan, 420015, Russia
  • University of Applied Sciences Zittau/Görlitz, IPM Theodor-Körner-Allee 16, 02763 Zittau, Germany
Bibliografia
  • [1] Babuska R. (1998): Fuzzy Modeling for Control. - Boston: Kluwer.
  • [2] Batyrshin I. (2002): On granular derivatives and solution of granular initial value problem. - Int. J. Appl. Math. Comp. Sci. (in this issue).
  • [3] Batyrshin I. and Panova A. (2001): On granular description of dependencies. - Proc. 9th Zittau Fuzzy Colloquium, Zittau, Germany, pp. 1-8.
  • [4] Batyrshin I., Zakuanov R. and Bikushev G. (1994): Expert system based on algebra of uncertainties with memory in process optimization. - Proc. 1st Int. FLINS Workshop Fuzzy Logic and Intelligent Technologies in Nuclear Science, Mol, Belgium, 1994, New York, World Scientific, pp. 156-159.
  • [5] Conte S.D. and de Boor C. (1972): Elementary Numerical Analysis. An Algorithmic Approach. - Tokyo: McGraw-Hill.
  • [6] De Boor C. (1978): A Practical Guide to Splines. - New York: Springer-Verlag.
  • [7] De Kleer J. and Brawn J. (1984): A qualitative physics based on confluences. - Artif. Intell., Vol. 24, pp. 7-83.
  • [8] Deogun J.S., Raghavan V. V., Sarkar A. and Sever H. (1997): Data mining: Research trends, challenges, and applications, In: Roughs Sets and Data Mining: Analysis of Imprecise Data (T.Y. Lin and N. Cercone, Eds.). - Boston, MA: Kluwer, pp. 9-45.
  • [9] Eiben A.E., Hinterding R. and Michalewicz Z. (1999): Parameter control in evolutionary algorithms. - IEEE Trans. Evol. Comput., Vol. 3, No. 2, pp. 124-141.
  • [10] Forbus K.D. (1984): Qualitative process theory. - Artif. Intell., Vol. 24, pp. 85-168.
  • [11] Friedman J.H. (1991): Multivariate adaptive regression splines. - Ann. Stat., Vol. 19, p. 1.
  • [12] Kacprzyk J. and Fedrizzi M., Eds. (1992): Fuzzy Regression Analysis. - Warsaw: Omnitech Press.
  • [13] Goldberg D.E. (1989): Genetic Algorithms in Search, Optimization and Machine Learning. - Ontario: Addison-Wesley.
  • [14] Goodrich M.T. (1994): Efficient piecewise-linear function approximation using the uniform metric. - Proc. 10-th ACM Symp. Computational Geometry (SCG), pp. 322-331.
  • [15] Huarng K. (2001): Effective lengths of intervals to improve forecasting in fuzzy time series. - Fuzzy Sets Syst., Vol. 123, pp. 387-394.
  • [16] Jang J.S.R., Sun C.T. and Mizutani E. (1997): Neuro-Fuzzy and Soft Computing. A Computational Approach to Learning and Machine Intelligence. - New York: Prentice Hall.
  • [17] Kivikunnas S. (1999): Overview of process trend analysis methods and applications. - Proc. Workshop Applications in Chemical and Biochemical Industry, Aachen, Germany.
  • [18] Klir G.J. and Folger T.A. (1988): Fuzzy Sets, Uncertainty, and Information. - London: Prentice-Hall.
  • [19] Kosko B. (1997): Fuzzy Engineering. - Upper Saddle River, NJ: Prentice-Hall.
  • [20] Loncaric S. (1998): A survey of shape analysis techniques. - Pattern Recogn., Vol. 31, No. 8, pp. 983-1001.
  • [21] Wang Li-X. (1997): A Course in Fuzzy Systems and Control. - Upper Saddle River, NJ: Prentice Hall.
  • [22] Wong S.V. and Hamouda A.M.S. (2000): Optimization of fuzzy rules design using genetic algorithm. - Adv. Eng. Softw., Vol. 31, pp. 251-262.
  • [23] Yu J.-R., Tzeng G.-H. and Li H.-L. (2001): General fuzzy piecewise regression analysis with automatic change-point detection. - Fuzzy Sets Syst., Vol. 119, pp. 247-257.
  • [24] Zadeh L.A. (1973): Outline of a new approach to the analysis of complex systems and decision processes. - IEEE Trans. Syst. Man. Cybern., Vol. 1, pp. 28-44.
  • [25] Zadeh L.A. (1975): The concept of a linguistic variable and its application to approximate reasoning. - Part I: Inf. Sci., Vol. 8, pp. 199-249; Part II: Inf. Sci., Vol. 8, pp. 301-357; Part III: Inf. Sci., Vol. 9, pp. 43-80.
  • [26] Zadeh L.A. (1996): Fuzzy logic = computing with words. - IEEE Trans. Fuzzy Syst., Vol. 4, No. 2, pp. 103-111.
  • [27] Zadeh L.A. (1997): Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. - Fuzzy Sets Syst., Vol. 90, pp. 111-127.
  • [28] Zadeh L.A. (1999): From computing with numbers to computing with words-from manipulation of measurements to manipulation of perceptions. - IEEE Trans. Circ. Syst. - 1: Fund. Th. Applic., Vol. 45, No. 1, pp. 105-119.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0001-0035
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