In this paper, we propose a fuzzy SVk -modes algorithm that uses the fuzzy k -modes clustering process to cluster categorical data with set-valued attributes. In the proposed algorithm, we use Jaccard coefficient to measure the dissimilarity between two objects and represent the center of a cluster with set-valued modes. A heuristic update way of cluster prototype is developed for the fuzzy par...

Discovery of Full functional dependencies from relations has been identified as an important database analysis technique. In order to deal with information inexactness, fuzzy techniques have extensively been integrated with different database models and theories. However, the information is often vague or ambiguous and very difficult to represent in implementing the application software. This p...

An approach based on multi-objective genetic algorithms is proposed to construct interpretable and precision fuzzy classification system from data. First, a multi-objective genetic algorithm is used to accomplish feature selection and dynamic grid partition with three objectives: maximization of precision, minimization of the number of features, and minimization of the number of fuzzy rules. Th...

This paper proposes a Self-Evolving Interval Type-2 Fuzzy Neural Network (SEIT2FNN) for nonlinear systems identification. The SEIT2FNN has both on-line structure and parameter learning abilities. The antecedent parts in each fuzzy rule of the SEIT2FNN are interval type-2 fuzzy sets and the fuzzy rules are of the Takagi-Sugeno-Kang (TSK) type. An on-line clustering method is proposed to generate...

Abstract We present a new type of fuzzy controller constructed with the B spline model and its applications in modelling and control Un like the other normalised parameterised set functions for de ning fuzzy sets B spline basis functions do not necessarily span from membership value to but possess the property partition of unity These B spline basis functions are automatically determined after ...

The ‘hnique selling point” of fuzzy systems is usually the interpretability of its rule base. However, very often only the U C C U T U C ~ of the rule base is measured and used to compare a fuzzy system to other solutions. We have suggested an index to measurz the interpretability of fuzzy rule bases for classification problems. However, the index can be used to describe the interpretability of...

In this paper we describe the design of a fuzzy logic controller for the ball and beam system using a modified Ant Colony Optimization (ACO) method for optimizing the type of membership functions, the parameters of the membership functions and the fuzzy rules. This is achieved by applying a systematic and hierarchical optimization approach modifying the conventional ACO algorithm using an ant s...

There are many methods for numerical solutions of integral equations. In various branches of science and engineering, chemistry and biology, and physics applications integral equation is provided by many other authors. In this paper, a simple numerical method using a fuzzy, for the numerical solution of the integral equation with the weak singular kernel is provided. Finally, by providing three...

Histograms are very useful for summarizing statistical information associated with a set of observed data. They are one of the most frequently used density estimators due to their ease of implementation and interpretation. However, histograms suffer from a high sensitivity to the choice of both reference interval and bin width. This paper addresses this difficulty by means of a fuzzy partition....

Remote sensing information is critical to preserve environments in tropical and mountain regions (e.g., costal hurricane mitigation or mountain forest management). In order to partition graphical information into meaningful regions and extract salient objects, segmentation is used to identify visual signatures based on similarity criteria. Due to atmospheric dispersing, nonlinear denoising is n...