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On spline based fuzzy transforms

Department of Mathematics, University of Latvia. On spline based fuzzy transforms. Irina Vaviļčenkova, Svetlana Asmuss. ELEVENTH INTERNATIONAL CONFERENCE ON FUZZY SET THEORY AND APPLICATIONS. Liptovský Ján, Slovak Republic , January 30 - February 3, 2012.

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On spline based fuzzy transforms

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  1. Department of Mathematics, University of Latvia On spline based fuzzy transforms Irina Vaviļčenkova, Svetlana Asmuss ELEVENTH INTERNATIONAL CONFERENCE ON FUZZY SET THEORY AND APPLICATIONS Liptovský Ján, Slovak Republic, January 30 - February 3, 2012

  2. This talk is devoted to F-transform (fuzzy transform). The core idea of fuzzy transform is inwrought with an interval fuzzy partitioning into fuzzy subsets, determined by their membership functions. In this work we consider polynomial splines of degree m and defect 1 with respect to the mesh of an interval with some additional nodes. The idea of the direct F-transform is transformation from a function space to a finite dimensional vector space. The inverse F-transform is transformation back to the function space.

  3. Fuzzy partitions

  4. The fuzzy transform was proposed by I. Perfilieva in 2003 and studied in several papers [1] I.Perfilieva, Fuzzy transforms: Theory and applications, Fuzzy Sets and Systems, 157 (2006) 993–1023. [2] I. Perfilieva, Fuzzy transforms, in: J.F. Peters, et al. (Eds.), Transactions on Rough Sets II, Lecture Notes in Computer Science, vol. 3135, 2004, pp. 63–81. [3] L. Stefanini, F- transform with parametric generalized fuzzy partitions, Fuzzy Sets and Systems 180 (2011) 98–120. [4] I. Perfilieva, V. Kreinovich, Fuzzy transforms of higher order approximate derivatives: A theorem , Fuzzy Sets and Systems 180 (2011) 55–68. [5] G. Patanè, Fuzzy transform and least-squares approximation: Analogies, differences, and generalizations, Fuzzy Sets and Systems 180 (2011) 41–54.

  5. Classical fuzzy partition

  6. An uniform fuzzy partition of the interval [0, 9] by fuzzy sets with triangular shaped membership funcions n=7

  7. Polynomial splines

  8. Quadratic spline construction

  9. Cubic spline construction

  10. An uniform fuzzy partition of the interval [0, 9] by fuzzy sets with cubic spline membership funcions n=7

  11. On additional nodes Generally basic functions are specified by the mesh but when we construct those functions with the help of polynomial splines we use additional nodes. where k directly depends on the degree of a spline used for constructing basic functions and the number of nodes in the original mesh

  12. Additional nodes for splines with degree 1,2,3...m (uniform partition)

  13. We consider additional nodes as parameters of fuzzy partition: the shape of basic functions depends of the choise of additional nodes

  14. Direct F – transform

  15. Inverse F-transform The error of the inverse F-transform

  16. Inverse F-transforms of the test function f(x)=sin(xπ/9)

  17. Inverse F-transform errors in intervals for the test function f(x)=sin(xπ/9) in case of different basic functions, 3/2 step.

  18. Inverse F-transform and inverse H-transform of the test function f(x)=sin(1/x) in case of linear spline basic functions, n=10 un n=20

  19. Inverse F-transform and inverse H-transform of the test function f(x)=sin(1/x) in case of quadratic spline basic functions, n=10 un n=20

  20. Inverse F-transform and inverse H-transform of the test function f(x)=sin(1/x) in case of cubic spline basic functions, n=10 un n=20

  21. Generalized fuzzy partition

  22. An uniform fuzzy partition and a fuzzy m-partition of the interval [0, 9]based on cubic spline membership funcions, n=7, m=3

  23. Numerical example

  24. Numerical example: approximation of derivatives

  25. Approximation of derrivatives

  26. F-transform and least-squares approximation

  27. Direct transform

  28. Direct transform matrix analysis • Matrix M1 for linear spline basic functions The evaluation of inverse matrix norm is true

  29. Matrix M2 for quadratic spline basic functions The evaluation of inverse matrix norm is true

  30. Matrix M3 for cubic spline basic functions The evaluation of inverse matrix norm is true

  31. Direct transform error bounds

  32. Inverse transform The error of the inverse transform

  33. Inverse transforms of the test function f(x)=sin(xπ/9) in case of quadratic spline basic functions, n=7

  34. Inverse transforms approximation errors of the test function f(x)=sin(xπ/9) in case of quadratic spline basic functions, for n=10, n=20, n=40.

  35. Inverse transforms of the test function f(x)=sin(xπ/9) in case of cubic spline basic functions, n=7

  36. Inverse transforms approximation errors of the test function f(x)=sin(xπ/9) in case of cubic spline basic functions, for n=10, n=20, n=40.

  37. Thank you for your attention!

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