Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some kinds...

Fuzzy sets theory has proven over the years to be a valuable tool for modeling uncertainty in engineering. It is used extensively in control, in expert systems and in rule-based models. However, applications to sensitivity analysis and regression are still few, mainly because there is no appropriate software available. A C++ library of objects has been developed to easily and efficiently introd...

A fuzzy control system based on a new method called maximal fuzzy similarity in fact the equivalence relation of Lukasiewicz logic is introduced to control an isolated pedestrian crossing signals The main goal in this multi objective optimization problem is to minimize both pedestrians and vehi cles delay and make the tra c ow as u ent and safe as possible Lukasiewicz many valued logic in known...

Abstract Assuming that ∗ is any operation defined on a product set X × Y and taking values on a set Z,it can be extended to intuitionistic fuzzy sets by means of the extended form of the Zadeh’s extension principle for the intuitionistic fuzzy sets. Given an IFS C of Z, it is here shown how to solve the equation A ∗ B = C (or A ∗ B ⊆ C) when an intuitionistic fuzzy subset A of X (or an intuitio...

Clustering algorithms have been utilized in a wide variety of application areas. One of these algorithms is the Fuzzy C-Means algorithm (FCM). One of the problems with these algorithms is the time needed to converge. In this paper, a Fast Fuzzy C-Means algorithm (FFCM) is proposed based on experimentations, for improving fuzzy clustering. The algorithm is based on decreasing the number of dista...

Aim of this paper is to develop an efficient fuzzy c-mean based segmentation algorithm to extract tumor region from MR brain images. First, cluster centroids are initialized through data analysis of tumor region, which optimizes the standard fuzzy cmean algorithm. Next, reconstruction based morphological operations are applied to enhance its performance for brain tumor extraction. The results s...

Fuzzy C-Mean (FCM) is an unsupervised clustering algorithm based on fuzzy set theory that allows an element to belong to more than one cluster. Where fuzzy means “unclear” or “not defined” and c denotes “clustering”. In FCM the number of cluster are randomly selected. [15] FCM is the advanced version of K-means clustering algorithm and doing more work than K-means. K-Means just needs to do a di...

In this paper,  we determine necessary and sufficient Tauberian conditions under which convergence in Pringsheim's sense of a double sequence of fuzzy numbers follows from its $(C,1,1)$ summability. These conditions are satisfied if the double sequence of fuzzy numbers is slowly oscillating in different senses. We also construct some interesting double sequences of fuzzy numbers.

This paper presents an application of fuzzy c-means clustering to designing the fuzzy logic controller for the temperature control in electric ceramics kiln. This research aims to controlling the temperature in firing step of burning the ceramic products which were coated with black, intensely red and green chemical substances. The experimental results show that the fuzzy c-means clustering des...

To a fuzzy choice function C we assign the indicators of revealed preference WAFRP (C), SAFRP (C) and the indicators of congruence WFCA(C), SFCA(C). These indicators measure the degree to which the fuzzy choice function C verifies the axioms of revealed preference WAFRP , SAFRP and of congruenceWFCA and SFCA, respectively.