g e y w o r d s F u z z y numbers, Convexity, Continuity, Convex fuzzy mappings, Linear ordering, Fuzzy Weirstrass theorem, Fuzzy optimization. 1. I N T R O D U C T I O N Let R n denote the n-dimensional Eucl idean space. The suppor t , supp(#) , of a fuzzy set # : R ~ I = [0, 1] is defined as supp(/~) = {x • R n I #(x) > 0}. A fuzzy set # : R n --~ I is called fuzzy convex if #(Ax + (1 A)y) _>...

Received (received date) Revised (revised date) Coping with uncertainty in dynamical systems has recently received some attention in artiicial intelligence (AI), particularly in the elds of qualitative and model-based reasoning. In this paper, we propose an approach to modelling and simulation of uncertain dynamics which is based on the following ideas: We consider (linguistic) descriptions of ...

[1] The availability of high resolution topography from LIDAR offers new opportunities for objectively extracting the channels directly from a DEM using local topographic information, instead of inferring them indirectly based on global criteria, such as area or area-slope threshold relationships. Here we introduce the use of wavelet filtering to delineate threshold curvatures for defining vall...

We interpret a type of fuzzy controller as an inter polator of B spline hypersurfaces B spline basis func tions of di erent orders are regarded as a class of mem bership functions MFs with some special properties These properties lead to several interesting conclusions about fuzzy controllers if such membership functions are employed to specify the linguistic terms of the input variables We sho...

Let E n be a sequence of experiments weakly converging to a limit experiment E. One of the basic objectives of asymptotic decision theory is to derive asymptotically "best" decisions in E n from optimal decisions in the limit experiment E. A central statement in this context is the Hajek-LeCam bound which represents a lower bound for the maximum risk of a sequence of decisions. It is given a si...

By connecting the fuzzy weighted average (FWA) and the generalized centroid (GC) of an interval type-2 fuzzy set we have arrived at a new αcut algorithm for solving the FWA problem, one that is monotonically and super-exponentially convergent. Our new algorithm uses the Karnik-Mendel (KM) algorithms to compute the FWA α-cut end-points. No faster algorithm for solving this problem exists to-date.