Probabilistic fuzzy systems combine a linguistic description of the system behaviour with statistical properties of data. It was originally derived based on Zadeh’s concept of probability of a fuzzy event. Two possible and equivalent additive reasoning schemes were proposed, that lead to the estimation of the output’s conditional probability density. In this work we take a complementary approac...

Study of fuzzy entropy on intuitionistic fuzzy sets (IFSs) were proposed, and analyzed. Built in uncertainty in IFSs, it is named as hesitance. It is contained in fuzzy membership function in itself by definition. Hence, designing fuzzy entropy is not easy because there is no general entropy definition about IFSs. By considering existing fuzzy entropy definitions, fuzzy entropy on IFSs is desig...

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valu...

Ranking of fuzzy numbers plays a very important role in linguistic decision making and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce nonintuitive results in certain cases. In this paper, some new approaches for ranking of trapezoidal fuzzy numbers are introduced. Keywords— Magnitude of...

Fuzzy measures have been widely used to determine the degrees of subjective importance of evaluation items. However, the leniency error may exist when most attributes are assigned unduly high ratings. Because respondents often assign similarly complimentary scores, errors of positive leniency make it difficult to differentiate the importance of decision attributes. To reduce positive leniency i...

In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspect of the problem, and as a result they may present incomplete preferences, i.e. some preference values may not be given or may be missing. In this paper we present a new Model for Group Decision...

Modeling case-base competence is a crucial issue in the field of CBR. In fact it has led to a number of important developments in CBR recently, the most well known being in case deletion and case-base visualization and authoring support. This is highlighted by Smyth and Keane’s competence model (called the S-K model in this paper), which was shown to be efficient. In this paper, we argue that t...