there are many situations for statistical process in which we have both random and vagueinformation. when uncertainty is due to fuzziness of information, fuzzy statistical control charts play animportant role in the monitoring process, because they simultaneously deal with both kinds of uncertainty.dealing with fuzzy characteristics using classical methods may cause the loss of information and ...

In this paper we study some models for dealing with Fuzzy Integer Linear Programming problems which have a certain lack of precision of a vague nature in their formulation. We present methods to solve them with either fuzzy constraints, or fuzzy numbers in the objective function or fuzzy numbers defining the set of constraints. These methods are based on the representation theorem and on fuzzy ...

Boosting is recognized as one of the most successful techniques for generating classifier ensembles. Typically, the classifier outputs are combined by the weighted majority vote. The purpose of this study is to demonstrate the advantages of some fuzzy combination methods for ensembles of classifiers designed by Boosting. We ran 2-fold cross-validation experiments on 6 benchmark data sets to com...

Statistical data are frequently not precise numbers but more or less non-precise, also called fuzzy. Measurements of continuous variables are always fuzzy to a certain degree. Therefore histograms and generalized classical statistical inference methods for univariate fuzzy data have to be considered. Moreover Bayesian inference methods in the situation of fuzzy a-priori information and fuzzy da...

A fuzzy approach to normalization methods for pattern verification is presented in this paper. For an input object and a claimed identity, a claimed pattern's score is calculated and compared with a given threshold to accept or reject the claimed pattern. Considering the pattern verification problem based on fuzzy set theory, the claimed pattern's score is viewed as a fuzzy membership function....