In this paper we present a fuzzy system based hyperspectral classi®er for automatic target identi®cation. The system is based on partitioning the spectral band space into clusters using a modi®ed fuzzy C-Means clustering algorithm. Classi®cation of each pixel is then carried out by calculating its fuzzy membership in each cluster. The results showed that the fuzzy hyperspectral classi®er is suc...

In this paper we present a fuzzy system based hyperspectral classi®er for automatic target identi®cation. The system is based on partitioning the spectral band space into clusters using a modi®ed fuzzy C-Means clustering algorithm. Classi®cation of each pixel is then carried out by calculating its fuzzy membership in each cluster. The results showed that the fuzzy hyperspectral classi®er is suc...

The problem of mining association rules for fuzzy quantitative items was introduced and an algorithm proposed in [5]. However, the algorithm assumes that fuzzy sets are given. In this paper we propose a method to find the fuzzy sets for each quantitative attribute in a database by using clustering techniques. We present a scheme for finding the optimal partitioning of a data set during the clus...

Fuzzy co-clustering is a method that performs simultaneous fuzzy clustering of objects and features. In this paper, we introduce a new fuzzy coclustering algorithm for high-dimensional datasets called Cosine-Distancebased & Dual-partitioning Fuzzy Co-clustering (CODIALING FCC). Unlike many existing fuzzy co-clustering algorithms, CODIALING FCC is a dualpartitioning algorithm. It clusters the fe...

slope stability analysis is an enduring research topic in the engineering and academic sectors. accurate prediction of the factor of safety (fos) of slopes, their stability, and their performance is not an easy task. in this work, the adaptive neuro-fuzzy inference system (anfis) was utilized to build an estimation model for the prediction of fos. three anfis models were implemented including g...

Given the current limitations in fuzzy clustering metric, the aim of this paper is to present new wasserstein metric based adaptive fuzzy clustering methods for partitioning symbolic interval data. Wasserstein metric shows adavantages in digging distribution information in symbolic interval data. Besides, the proposed fuzzy clustering methods also emphasize correlation structure between indices...

Fuzzy Finite Tree Automata (FFTA) are natural generalizations of fuzzy automata on words. FFTA accept fuzzy tree languages and are used in many areas of mathematics, computer science and engineering. Therefore, it is important to design algorithms that reduce the size (number of states) of automata, and find the minimal FFTA that recognizes the same language as a given FFTA. We present two mini...

In this paper we propose a learning method of fuzzy if-then rules for pattern classification problems. We assume that each training pattern has a weight that describes its importance. The antecedent part of fuzzy if-then rules are specified by partitioning each attributes into fuzzy sets while the consequent class and the degree of certainty of the fuzzy if-then rules are determined from the co...

To apply fuzzy logic, two major tasks need to be performed: the derivation of production rules and the determination of membership functions. These tasks are often difficult and time consuming. This paper presents an algorithmic method for generating membership functions and fuzzy production rules; the method includes an entropy minimization for screening analog values. Membership functions are...