A description of quantitative dependencies by a novel type of fuzzy rules like ‘If X is SMALL then Y is QUICKLY INCREASING’ is considered. The use of such rules for representation of perception based and numerical information about dependencies between variables is discussed. These rules are based on a granulation of directions of function change or slope values. Perception based information gi...

Many energy-aware routing protocols have been proposed for wireless sensor networks. Most of them are only energy savers and do not take care about energy balancing. The energy saver protocols try to decrease the energy consumption of the network as a whole; however the energy manager protocols balance the energy consumption in the network to avoid network partitioning. This means that energy s...

A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. Their learning procedure is carried out with different parameters that define a nature of cluster borders’ blurriness. Clusters’ quality is estimated in an online m...

The paper is devoted to the smoothing of discrete functions using the fuzzy transform (shortly, Ftransform) introduced by Perfilieva in [1]. We generalize a smoothing filter based on the fuzzy transform proposed in [2] to obtain a better control on the smoothed functions. For this purpose, a generalization of the concept of fuzzy partition is suggested and the smoothing filter is defined as a c...

Thresholding-based techniques have been widely used in image segmentation. The selection of appropriate threshold is a very significant issue for image thresholding. In this paper, a new image histogram thresholding method based on fuzzy partition and maximum correlation criterion is presented. In the proposed approach, the regions, i.e. object and background, are considered ambiguous in nature...

Rough set theory has attracted much attention in modeling with imprecise and incomplete information. A generalized approximation space, called fuzzy probability approximation space has been proposed by introducing probability into fuzzy approximation space. The novel definition combines three types of uncertainty into a model. Information or knowledge is considered as a partition of the univers...

Clustering algorithms have been widely used artificial intelligence, data mining and machine learning, etc. It is unsupervised classification and is divided into groups according to data sets. That is, the data sets of similarity partition belong to the same group; otherwise data sets divide other groups in the clustering algorithms. In general, to analysis interval data needs Type II fuzzy log...

In this paper, a novel modified evolutionary fuzzy clustering has been proposed. This technique exploits parameters such as Minkowski distance, Xie Beni index, and classification entropy. These parameters turn to account to polish the performance measure by incorporating shape of the cluster, class compactness and partitioning quality respectively in standard Fuzzy C-Means clustering. Moreover,...

An application of fuzzy modeling to the problem of telecommunications time-series prediction is proposed in this paper. The model building process is a two-stage sequential algorithm, based on Subtractive Clustering (SC) and the Orthogonal Least Squares (OLS) techniques. Particularly, the SC is first employed to partition the input space and determine the number of fuzzy rules and the premise p...

Some methods of fuzzy clustering need to use a priori knowledge about the number of fuzzy classes or some other information about the possible distribution of the clusters. A way to improve these methods is to use hierarchical clustering as a preprocessing of the data. This approach does not provide a simple partition of the data set, but a hierarchy of them. In this paper we define several mea...