In this paper, we investigate a new method to handle multiple attribute group decision making (MAGDM) problems based on combined ranking value under interval type-2 fuzzy environment, in which all the attribute values provided by experts take the form of interval type-2 fuzzy sets (IT2FSs). We first introduce some basic concepts and related operational laws on IT2FSs. Then, we put forward three...

Parsimony is very important in system modeling as it is closely related to model interpretability. In this paper, a scheme for constructing accurate and parsimonious fuzzy models by generating distinguishable fuzzy sets is proposed, in which the distinguishability of input space partitioning is measured by a so-called “local” entropy. By maximizing this entropy measure the optimal number of mer...

In this paper, we propose a combined method for recognition of multi-font Persian numeral characters. At first, the binary image of a character is divided into a fixed number of sub-images called boxes. The average vector distance and angle of each box are computed as features. These features have some variations in different fonts of any character. So, we can employ the fuzzy sets to face with...

The paper introduces entropy as a measure for 1D signals. We propose as entropy measure the relationship between the crest of the signal (i.e. its portion contained between the absolute minimum and maximum) and the energy of the signal. A linear transformation of 2D signals into 1D signals is also illustrated. The experimental results are compared to several fuzzy entropy measures and other wel...

In this paper, we research the relationship between entropy and similarity of interval valued intuitionistic fuzzy sets in detail and prove eight theorems that entropy and similarity of interval valued intuitionistic fuzzy sets can be transformed to each other based on their axiomatic definitions. Finally, we propose some formulaes to calculate entropy and similarity of interval valued intuitio...

Data clustering is one of the important data mining methods. It is a process of finding classes of a data set with most similarity in the same class and most dissimilarity between different classes. The well known hard clustering algorithm (K -means) and Fuzzy clustering algorithm (FCM) are mostly based on Euclidean distance measure. In this paper, a comparative study of these algorithms with d...