Fuzzy time series (FTS) is one of the forecasting methods that has been developed until now. The fuzzy a method uses concept logic, which Song and Chissom first introduced. Markov chain in defuzzification. determination length interval plays an important role forming logic relationship (FLR), this FLR will be used to determine value. One can average-based. However, several studies use partition...

In order to avoid the usual binary definition of poverty, a fuzzy set approach to poverty measurement has been used. The poor are typically analyzed by partitioning the total population in three mutually exclusive groups around the poverty line. This builds on fuzzy sets theory whereby the definition of the threshold of who is poor or non-poor, is fuzzy. This paper presents a method that consis...

Many image processing applications involve a pattern classification stage. In this paper we propose a classifier based on fuzzy if–then rules that allows the incorporation of weighted training patterns which can be used to adjust the sensitivity of the classification with respect to certain classes. The antecedent part of fuzzy if–then rules are specified by partitioning each attributes into fu...

This article discusses a rule-based fuzzy model for the identi cation of nonlinear MISO (multiple input, single output) systems. The dis cussed method of fuzzy modeling consists of two parts: structure modeling, i.e. determing the num ber of rules and input variables involved respec tively, and parameter optimization, i.e. optimizing the location and form of the curves which describe the fuz...

In this paper we present a hybrid approach based on combining fuzzy clustering, seed region growing, and Jaccard similarity coefficient algorithms to measure gray (GM) and white matter tissue (WM) volumes from magnetic resonance images (MRIs). The proposed algorithm incorporates intensity and anatomic information for segmenting of MRIs into different tissue classes, especially GM and WM. It sta...

In this paper a general fuzzy approach for segmentation-based classification is proposed. Traditional segmentation techniques focus on partitioning imagery into image-objects with well-defined boundaries. Instead, the proposed methodology aims to produce and analyze fuzzy image-regions expressing degrees of membership to different target classes. This approach, called Fuzzy Image-Regions Method...

This article discusses a rule-based fuzzy model for the identiication of nonlinear MISO (multiple input, single output) systems. The diss cussed method of fuzzy modeling consists of two parts: structure modeling, i.e. determing the numm ber of rules and input variables involved respecc tively, and parameter optimization, i.e. optimizing the location and form of the curves which describe the fuz...