This work explores the applicability of fuzzy clustering methods to the segmentation of sea surface temperature (SST) images for the automatic identification of upwelling areas in the coastal ocean of Portugal. This has been done by exploring the fuzzy c-means algorithm. Visualization of fuzzy c-partitions is achieved by means of color mapping. Selection of the best c-partition that represents ...

 Fuzzy C-Means (FCM) is an unsupervised clustering method that has been used extensively in data analysis and image segmentation. The defuzzification of the fuzzy partition of FCM is usually done using the maximum membership degree principle which may not be appropriate for some real-world applications. In this paper, we present a new algorithm that generates a probabilistic model of the fuzzy...

In this paper, we will illustrate the F-transform based on generalized fuzzy partitions as a tool for expectile smoothing. This allows to represent a time series in terms of a fuzzy-valued function whose level-cuts are modeled by F-transform and estimated by expectile regression. The proposed methodology is illustrated on real economic and …nancial time series. Keywords: Fuzzy Transform, Expect...

The paper is a short overview of the generalization of Groenendijk-Stokhof’s system of erotetic logic(also known as the partition semantics of questions) to fuzzy questions. Fuzzy intensional semantics,necessary for Groenendijk-Stokhof’s system, is developed within Henkin-style second-order fuzzy logic,which is introduced first. Our attention is restricted to fuzzy yes-no questions.

This paper discusses the application of generating fuzzy rules with word computing in genetic fuzzy classification system, and proposes a new method to design genetic fuzzy classification system. The new algorithm generates initial fuzzy rules population with expertise of the randomly selecting samples, and adds mutating parameters to adjust the shape of membership function of fuzzy partition i...

In this paper a new identification method for non-linear hybrid systems that have mixed continuous and discrete states by using fuzzy clustering and principal component analysis is described. The method first determines the hybrid characteristic of the system inspired by an inverse form of the merge method for clusters, which makes it possible to identify the unknown switching points of a proce...

This paper proposed a model to predict the stock price based on combining Self-Organizing Map (SOM) and fuzzy – Support Vector Machines (f-SVM). Extraction of fuzzy rules from raw data based on the combining of statistical machine learning models is the base of this proposed approach. In the proposed model, SOM is used as a clustering algorithm to partition the whole input space into several di...

Thresholding is an important topic for image processing, pattern recognition and computer vision. Selecting thresholds is a critical issue for many applications. The fuzzy set theory has been successfully applied to many areas, such as control, image processing, pattern recognition, computer vision, medicine, social science, etc. It is generally believed that image processing bears some fuzzine...

In the recent past Kernelized Fuzzy C-Means clustering technique has earned popularity especially in the machine learning community. This technique has been derived from the conventional Fuzzy C-Means clustering technique of Bezdek by defining the vector norm with the Gaussian Radial Basic Function instead of a Euclidean distance. Subsequently the fuzzy cluster centroids and the partition matri...