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...

This paper focuses on applications of fuzzy segmentation in region indexing and image retrieval. First, our algorithm of fuzzy segmentation is shortly explained. Some features characterizing a fuzzy region are then defined, and a distance between fuzzy regions is proposed. This distance can be used to rank regions on color and/or shape features or to perform a partial request from a image, requ...

this study considers an eoq inventory model with advance payment policy in a fuzzy situation by employing two types of fuzzy numbers that are trapezoidal and triangular. two fuzzy models are developed here. in the first model the cost parameters are fuzzified, but the demand rate is treated as crisp constant. in the second model, the demand rate is fuzzified but the cost parameters are treated ...

The concept of trapezoidal valued hesitant fuzzy set is introduced. Notion for distance between any two trapezoidal valued hesitant fuzzy elements is given. Using this proposed distance measure, we extend the technique for order preference by similarity to ideal solution for trapezoidal valued hesitant fuzzy sets. An example is constructed to show usefulness of this extension for multicriteria ...

Most existing distances between intuitionistic fuzzy sets are defined in linear plane representations in 2D or 3D space. Here, we define a new interpretation of intuitionistic fuzzy sets as a restricted spherical surface in 3D space. A new spherical distance for intuitionistic fuzzy sets is introduced. We prove that the spherical distance is different from those existing distances in that it is...

In this paper, the fuzzy scalar median (FSM) is proposed, defined by using ordering of fuzzy numbers based on fuzzy minimum and maximum operations defined by using the extension principle. Alternatively, the FSM is defined from the minimization of a fuzzy distance measure, and the equivalence of the two definitions is proven. Then, the fuzzy vector median (FVM) is proposed as an extension of ve...

A general framework for minimal distance methods is presented. Radial Basis Functions (RBFs) and Multilayer Perceptrons (MLPs) neural networks are included in this framework as special cases. New versions of minimal distance methods are formulated. A few of them have been tested on a real-world datasets obtaining very encouraging results.

We develop a formal framework to deal with code constructions in a fuzzy setting. Strings are modeled as fuzzy sets and an adequate concept of distance is defined. Moreover, we study fuzzy codebooks from the point of view of their minimum distance. This fuzzy framework is then used to model the DNA word design problem, i.e. the construction of particular codes of DNA strings that are used in mo...

We introduce the notion of the Gromov-Hausdorff fuzzy distance between two non-Archimedean fuzzy metric spaces (in the sense of Kramosil and Michalek). Basic properties involving convergence and the fuzzy version of the completeness theorem are presented. We show that the topological properties induced by the classic Gromov-Hausdorff distance on metric spaces can be deduced from our approach.

In this paper, we use the weighted distance between fuzzy numbers to investigate the approximation of arbitrary fuzzy numbers by weighted distance. We then discuss properties of the approximation strategy including continuity, translation invariance, scale invariance and identity and give an application to the generation of fuzzy partitions. 2006 Elsevier Inc. All rights reserved.