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

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

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

9. Polynomial splines

10. Quadratic spline construction

11. Cubic spline construction

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

13. Generally basic functions are specified by the mesh but when we construct those functions with the help of polynomial splines we use additional nodes. On 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

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

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

16. Direct F – transform

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

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

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

20. 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

21. 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

22. 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

23. Generalized fuzzy partition

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

28. Numerical example

29. Numerical example: approximation of derivatives

30. Approximation of derrivatives

31. F-transform and least-squares approximation

32. Direct transform

34. Direct transform matrix analysis Matrix M 1 for linear spline basic functions The evaluation of inverse matrix norm is true

35. Matrix M 2 for quadratic spline basic functions The evaluation of inverse matrix norm is true

36. Matrix M 3 for cubic spline basic functions The evaluation of inverse matrix norm is true

37. Direct transform error bounds

39. Inverse transform The error of the inverse transform

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

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

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

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