This paper shows a proposal for Type-reduction of an Interval Type-2 fuzzy set composed from α-cuts done over its primary membership functions. The definition of available Type-reduction methods for Interval Type-2 fuzzy sets are based on an homogeneous subdivision of the universe of discourse, so we propose an approximation algorithm for Type-reduction of an Interval type-2 fuzzy set through i...

This article describes a Hopfield-Tank model of the Euclidean traveling salesman problem (using Aiyer’s subspace approach) that incorporates a “fuzzy” interpretation of the rows of the activation array. This fuzzy approach is used to explain the network’s behavior. The fuzzy interpretation consists of computing the center of mass of the positive activations in each row. This produces real numbe...

Abstract This paper describes the research procedures adopted in developing a triangular fuzzy number scale based on semantic of MACBETH (Measuring Attractiveness by Categorical Based Evaluation Technique). The objective was to mathematically treat uncertainty and subjectivity linguistic variables used assess decision problem. A matrix initially obtained maker’s assessment given context analysi...

Medical image fusion is one of the popular research topics. Medical image fusion generally means the matching and fusion between two or more images of the same lesion area from different medical imaging equipment. Medical image fusion aims to obtain complementary information and also increase the amount of information. Medical image fusion technique is to combine the information of a variety of...

This paper presents a latest survey of different technologies used in medical image segmentation using Fuzzy C Means (FCM).The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. To update the study of image segmentation the survey has performed. The techniques used for this survey are Brain Tumor Detection Using Segmentation Bas...

In this paper, the theoretical foundations of generalised fuzzy Bayesian Network based on Vectorial Centroid defuzzification is introduced. The extension of Bayesian Network takes a broad view by examples labelled by a fuzzy set of attributes, instead of a classical set. Combining fuzzy set theory and Bayesian Network’s knowledge allows the use of fuzzy variables or attributes that widely used ...

Remote Sensing Imagery is used by the Government and private agencies for the wide range of applications from military to farm development. Fuzzy c-means clustering is an effective algorithm, but the random selection in center points makes iterative process falling into the local optimal solution easily. In this Paper, a novel clustering method is developed using GA based clustering techniques....

Fuzziness (entropy) is a commonly used measure of uncertainty for type-1 fuzzy sets. For interval type-2 fuzzy sets (IT2 FSs), centroid, cardinality, fuzziness, variance and skewness are all measures of uncertainties. The centroid of an IT2 FS has been defined by Karnik and Mendel. In this paper, the other four concepts are defined. All definitions use a Representation Theorem for IT2 FSs. Form...

A method for improving centroid-based clustering is suggested. The improvement built on diversification of the k-means++ initialization. algorithm claimed to be a better version k-means tested by computational set-up, where dataset size, number features, and clusters are varied. statistics obtained testing have shown that, in roughly 50 % instances cluster, outputs worse results than with rando...

Many applications of fuzzy set theory require defuzzification and ranking approaches based on alpha level sets because exact membership functions may not always be available. In this article, we have assumed that exact membership functions can be approximated using piecewise linear functions based on alpha level sets and derived two analytical formulas to meet such a requirement. The two formul...