One of the extensions of binary morphology to greyscale images is given by the classical fuzzy mathematical morphology. Interval-valued fuzzy mathematical morphology further extends the latter theory by now also allowing uncertainty in the grey values of the image. In this paper, the decomposition of the intervalvalued fuzzy morphological operations into their binary counterparts is studied bot...

Rough set theory, introduced by Pawlak [10], is a mathematical formalism dealing with uncertainty and to some extent overlapping fuzzy set theory introduced by Zadeh [12]. In fuzzy set theory vagueness is expressed by a membership function. The rough set theory approach is based on indiscernibility relations and approximations. A major advantage of rough set theory is that it needs no prelimina...

In this paper we introduce a neural network implementation of fuzzy mathematical morphology operators and apply it to image denoising. Using a supervised training method and differentiable equivalent representations for the fuzzy morphological operators, we derive efficient adaptation algorithms to optimize the structuring elements. We can then design fuzzy morphological filters for processing ...

Weanalyse the representability of different classes of binary relations on a set bymeans of suitable fuzzy numbers. In particular, we show that symmetric triangular fuzzy numbers can be considered as the best codomain to represent interval orders. We also pay attention to the representability of other classes of acyclic binary relations. © 2005 Elsevier B.V. All rights reserved.

For theoretical fuzzy control it is a well known strategy to transform a system of control rules into a system of relation equations. Because these systems of relation equations are not always solvable, solvability criteria and approximate solutions have been discussed. We reconsider some of the results in this field and extend them using more recent results on t-norm based fuzzy logics and on ...

In this paper I investigate the System of fuzzy relation equations with inf → composition. I compare this system with the system of fuzzy relation equations with sup − ∗-composition. The purpose of this contribution is to show that there are many things which are common for both systems with sup−∗ and inf →-composition operations. K e y w o r d s: System of fuzzy relation equations; sup − ∗-com...

A novel scheme for developing, at low computational cost, neural-fuzzy classifiers based on large-scale, model-based exemplars is outlined. The new method extends the approach that Bezdek applied to train a neural net (NN) Sobel edge classifier by training the NN on the complete population of 3x3 binary image prototypes scored to fuzzy values by a classical operator. We first show that, replaci...

Classical fuzzy mathematical morphology is one of the extensions of original binary morphology to greyscale morphology. Recently, this theory was further extended to interval-valued fuzzy mathematical morphology by allowing uncertainty in the grey values of the image and the structuring element. In this paper, we investigate the construction of increasing interval-valued fuzzy operators from th...

First, Similarity Of Fuzzy Ontology Generation For Semantic Web, Fuzzy Formal Concept Analysis incorporates fuzzy logic into Formal Concept Analysis (FCA) to form a fuzzy concept lattice. The fuzzy ontology is an extension of the domain ontology for solving the uncertainty problems. Fuzzy Conceptual Clustering then constructs the concept hierarchy from the fuzzy concept lattice. Finally, Fuzzy ...

Because of its inherently asymmetric nature, set-theoretic analysis offers many interesting contrasts with analysis based on correlations. Until recently, however, social scientists have been slow to embrace set-theoretic approaches. The perception was that this type of analysis is restricted to primitive, binary variables and that it has little or no tolerance for error. With the advent of ‘‘f...