Although fuzzy support vector machine introduces the fuzzy membership degree in maximizing the margin and improves performance of classifier, it has not fully considered the position of training samples in the margin. In this paper, a double margin (rough margin) based fuzzy support vector machine (RFSVM) algorithm is presented by introducing rough set into fuzzy support vector machine. Firstly...

A new interactive segmentation method that combines fuzzy connected object extraction and edge detection is proposed. Fuzzy connectedness is a global fuzzy relation, which effectively captures fuzzy “hanging togetherness” of image elements. First, by selecting the seed point, fuzzy connectedness value between each image element and the seed point is computed via dynamic programming. Then, throu...

This chapter develops a sliding mode and fuzzy logic-based speed controller, which is named adaptive fuzzy sliding-mode controller (AFSMC) for an indirect fieldoriented control (IFOC) of an induction motor (IM) drive. Essentially, the boundary layer approach is the most popular method to reduce the chattering phenomena, which leads to trade-off between control performances, and chattering elimi...

This paper gives some known theoretical results about fuzzy rule-based classifiers and offers a few new ones. The ability of Takagi-Sugeno-Kang (TSK) fuzzy classifiers to match exactly and to approximate classification boundaries is discussed. The lemma by Klawonn and Klement about the exact match of a classification boundary in R (2) is extended from monotonous to arbitrary functions. Equivale...

In Geoinformation systems (GIS) there is a need to model spatial regions with indeterminate boundary and under uncertainties. Although fuzzy logic methods are of great interest in many GIS applications, the traditional fuzzy logic has two important deficiencies. First, to apply the fuzzy logic, we need to assign, to every property and for every value, a crisp membership function. Second, fuzzy ...

In this paper we introduce a fuzzy constraint linear discriminant analysis (FC-LDA). The FC-LDA tries to minimize misclassification error based on modified perceptron criterion that benefits handling the uncertainty near the decision boundary by means of a fuzzy linear programming approach with fuzzy resources. The method proposed has low computational complexity because of its linear character...

Methods like DBSCAN are widely used in the analysis of spatial data. These methods are based on the neighborhood relations which use distance between points. However, these neighborhood relations consider to have at least a certain number of neighbors within a definite boundary. In this proposed work such a neighborhood analysis is done by using the benefits of fuzzy sets theory. Usage of fuzzy...

In this paper, homogeneous regions formed by using multiscale intuitionistic fuzzy set is compared with homogeneous regions formed by multiscale representation of an image. The later approach does not consider the equivocal nature of the real world objects and is not accurate enough to measure the homogeneity in a real world scenario. By applying the theories of scale-space along with intuition...

This paper aims at analyzing a non-singleton fuzzy logic classifier (NSFLC) and assessing its ability to cope with uncertainties in pattern classification problems. The analysis demonstrate that the NSFLC has fuzzy classification boundary and noise suppression capability. These characteristics means that the NSFLC is particulary suitable for problems where the boundaries between classes is non-...

This paper presents a new technique for generating a set of fuzzy rules that can characterize the non-self space (abnormal) using only self (normal) samples. Because, fuzzy logic can provide a better definition of the boundary between normal and abnormal, it can increase the accuracy in solving the anomaly detection problem. Experiments with synthetic and real data sets are performed in order t...