Fuzzy representations of a real-valued random variable have been introduced with the aim of capturing relevant information on the distribution of the variable, through the corresponding fuzzy-valued mean value. In particular, characterizing fuzzy representations of a random variable allow us to capture the whole information on its distribution. One of the implications from this fact is that tes...

Propagating possibilistic and probabilistic variables through a mapping yields a fuzzy random variable. We propose a method to attach probability intervals to events pertaining to the output variable. We show that this method is consistent with classical approaches to fuzzy random variables and that the obtained probability interval is the mean value of the fuzzy probability defined by viewing ...

The expected value and variance of a fuzzy variable have been well studied in the literature, and they provide important characterizations of the possibility distribution for the fuzzy variable. In this paper, we seek a similar characterization of the joint possibility distribution for a pair of fuzzy variables. In view of the success of introducing the expected value and variance as fuzzy inte...

In fuzzy domain, a variable (vague linguistic term) often depends not only on a single variable but on more then one variables. In such a situation multiple regression analysis is more appropriate than simple regression analysis involving one independent variable. This paper introduces fuzzy multiple regression equations of fuzzy sets those are treated as a variable with certain values assigned...

The fuzzy sets defining the linguistic variable values can be seen as a fuzzy partition of the linguistic variable. The membership functions obtained using fuzzy clustering algorithms are defined with respect to the group prototypes, and they cannot be used to define the linguistic variable values. We introduce several criteria to pass from the clustering membership functions to the linguistic ...

This paper attempts to present the new approach to design sufficient number of systematic fuzzy linguistics in matrix form and map the Fuzzy Linguistic Variable Matrix, which contains linguistic terms, into numeric domain using Fuzzy Normal Distribution based on the Parabola-based Membership Function. Existing fuzzy set theory is difficult to design the systematic and sufficient fuzzy linguisti...

the purpose of this paper is to introduce the concept of l-fuzzybilinear operators. we obtain a decomposition theorem for l-fuzzy bilinearoperators and then prove that a l-fuzzy bilinear operator is the same as apowerset operator for the variable-basis introduced by s.e.rodabaugh (1991).finally we discuss the continuity of l-fuzzy bilinear operators.

We present a new novel method of automatically generating a multi-variable fuzzy inference system from given sample sets. We rst decompose the sample set, say , into a cluster of sample sets associated with the given input variables, then compute the associated fuzzy rules and membership functions for each variable, independent of the other variables, by solving a single input multiple outputs ...

In this communication we will consider hypothesis-tests for the (fuzzy-valued) mean value of a fuzzy random variable in a population. For this purpose, we will make use of a generalized metric for fuzzy numbers, and we will develop two different approaches for the case of fuzzy random variables taking on a finite number of possible values, both leading to close statistical inferences.

In this paper the conventional fuzzy k-modes algorithm for clustering categorical data is extended by representing the clusters of categorical data with fuzzy centroids instead of the hard-type centroids used in the original algorithm. Use of fuzzy centroids makes it possible to fully exploit the power of fuzzy sets in representing the uncertainty in the classification of categorical data. To t...