− Using fuzzy context-free grammars one can easily describe a finite number of ways to derive incorrect strings together with their degree of correctness. However, in general there is an infinite number of ways to perform a certain task wrongly. In this paper we introduce a generalization of fuzzy context-free grammars, the so-called fuzzy context-free K-grammars, to model the situation of maki...

In this paper, necessary and sufficient conditions are given for the k-regularity of block fuzzy matrices in terms of the schur complements of its k-regular diagonal blocks. A formula for k-g-inverse of a block fuzzy matrix is established. A set of conditions for a block matrix to be expressed as the sum of k-regular block matrices is obtained. Mathematics Subject Classification: 15B15; 15A09

In this paper proposes different conventional and fuzzy based clustering techniques for fault detection and isolation in process plant monitoring. Process plant monitoring is very important aspect to improve productiveness and efficiency of the product and plant. This paper takes a case study of plant data and implements K means algorithm and fuzzy C means algorithm to cluster the relevant data...

With the large volume of electronic portal images acquired and stringent time constraints, it is no longer feasible to follow the convention whereby the radiation oncologist reviews and approves or rejects all portals. For that purpose we have developed a portal image classifier based on the fuzzy k-nearest neighbour (k-NN) algorithm. Each portal image is represented by a feature vector that co...

One of the most known and effective methods in supervised classification is the K-Nearest Neighbors classifier. Several approaches have been proposed to enhance its precision, with the Fuzzy K-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. However, despite its good behavior, Fuzzy-kNN lacks of a method for properly defining several mechanisms regarding the repres...

For any two points P = (p (1) ,p (2) ,...,p (n)) and Q = (q (1) ,q (2) ,...,q (n)) of R n , we define the crisp vector → PQ = (q (1) −p (1) ,q (2) −p (2) ,...,q (n) −p (n)) = Q(−)P. Then we obtain an n-dimensional vector space E n = { → PQ | for all P,Q ∈ R n }. Further, we extend the crisp vector into the fuzzy vector on fuzzy sets of R n. Let D, E be any two fuzzy sets on R n and define the f...

State minimization is a fundamental problem in automata theory. The problem is also of great importance in the study of fuzzy automata. However, most work in the literature considered only state reduction of fuzzy automata, whereas the state minimization problem is almost untouched for fuzzy automata. Thus in this paper we focus on the latter problem. Formally, the decision version of the minim...

Atanassov K. , Intuitionistic Fuzzy Sets. , VII ITKR's Section, Sofia, June 1983. Hashimoto H. , Sub-inverses of fuzzy Matrices, Fuzzy Sets and Systems, Vol. 12(1984),155-168. Murugadas P and Lalitha K. , Dual Implication Operator in Intuitionistic Fuzzy Matrices, International conference on Mathematical Modelling and its Application-2012. Sriram S and Murugadas P. , Sub-inverses of Intuit...

A survey of various link analysis and clustering algorithms such as Page Rank, Hyperlink-Induced Topic Search, Weighted Page Rank based on Visit of Links K-Means, Fuzzy K-Means. Ranking algorithms illustrated, Weighted Page Rank is more efficient than Hyperlink-induced Topic Search Whereas clustering algorithms has described Fuzzy Soft, Rough K-Means is a mixture of Rough K-Means and fuzzy soft...

We present a novel approach for Fuzzy-Input Fuzzy-Output classification. One-Against-All Support Vector Machines are adapted to deal with the fuzzy memberships encoded in fuzzy labels, and to also give fuzzy classification answers. The mathematical background for the modifications is given. In a benchmark application, the recognition of emotions in human speech, the accuracy of our F-SVM approa...