The first published result in fuzzy rule interpolation was the α-cut based fuzzy rule interpolation, termed as KH fuzzy rule interpolation, originally devoted for complexity reduction. Some deficiencies of this method was presented later, such as subnormal conclusion for certain configuration of the involved fuzzy sets. However, since that several conceptually different fuzzy rule interpolation...

The paper focuses on the fuzzy extensions of two data analysis methods, namely the fuzzy cluster analysis and the fuzzy interpolation of spatial data (the so-called fuzzy kriging) and their suitability for ecological applications. Both extensions utilize exact (crisp) measurement data as well as imprecise data defined as fuzzy numbers or fuzzy vectors. Fuzzy clustering of fuzzy data (conical fu...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

We consider the interpolation problem for functions whose range and whose domain both consist of convex or fuzzy subsets of a real Euclidean space. This problem arises in fuzzy controlling, namely when the functional dependence between two fuzzy vectors is known only for finitely many cases. To have a criterion for an appropriate choice of an interpolation function, we generalise the well-known...

In this paper, rst a design is proposed for representing fuzzy polynomials withinput fuzzy and output fuzzy. Then, we sketch a constructive proof for existenceof such polynomial which can be fuzzy interpolation polynomial in a set given ofdiscrete points rather than a fuzzy function. Finally, to illustrate some numericalexamples are solved.

A voting model semantics for fuzzy sets is assumed with the corresponding ideas of mass assignment theory. The use of fuzzy sets in this way will provide better interpolation, greater knowledge compression, less dependence on the effects of noisy data than if only crisp sets were used. We will see how easy and useful it is to use successful inference methods such as decision trees, probabilisti...

Fuzzy rule based systems have been very popular in many engineering applications. In petroleum engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the petrophysical properties prediction model. However, when generating fizzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than one input varia...