Generalized regression for fuzzy rule bases using the Hough transform

Joseph M. Barone; Dimitar P. Fileu

doi:10.1117/12.165035

22 December 1993 Generalized regression for fuzzy rule bases using the Hough transform

Joseph M. Barone, Dimitar P. Fileu

Proceedings Volume 2061, Applications of Fuzzy Logic Technology; (1993) https://doi.org/10.1117/12.165035
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States

Abstract

The extended Hough transform permits weight functions of arbitrary type and complexity to help guide the choice of a `regression' line in polar coordinate space. This paper suggests that this transform may be helpful in locating the best linear approximation to gaps and areas of conflict in fuzzy rule bases. Using the sliding mode approximation of a fuzzy controller as an example, this paper shows how global properties of the rule base can be used to help guide the search for good approximations. The notion of `representativeness' of centroids and its effect on regression via the Hough transform is also considered. Finally, a different approach based on OWA operators is discussed briefly.

Citation Download Citation

Joseph M. Barone and Dimitar P. Fileu "Generalized regression for fuzzy rule bases using the Hough transform", Proc. SPIE 2061, Applications of Fuzzy Logic Technology, (22 December 1993); https://doi.org/10.1117/12.165035

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