Results 271 to 280 of about 20,160 (306)
Some of the next articles are maybe not open access.

On interval fuzzy S-implications

Information Sciences, 2010
Let \(\mathbb{U}=\{[a,b] \;| \;0\leq a\leq b\leq 1\}\) denote the set of subintervals of the unit interval. An interval S-implication is a function from \(\mathbb{U}^2\) to \(\mathbb{U}\) based on the functional \(\mathbb{I_{S,N}}(X,Y)=\mathbb{S}(\mathbb{N}(X),Y)\), where \(\mathbb{S}\) is an interval t-conorm and \(\mathbb{N}\) is an interval fuzzy ...
Benjamín R. C. Bedregal   +3 more
openaire   +1 more source

On the implication operator in fuzzy logic

Information Sciences, 1983
The implication operator in fuzzy logic can be translated into a suitable fuzzy relation in several ways. The problem is which translation/relation to choose. This question has been discussed by several authors so far. In this article various properties of relations, both already known and newly introduced, are thoroughly investigated, e.g.
openaire   +2 more sources

On rational fuzzy implication functions

2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016
The proposal of new classes of fuzzy implication functions must take into account the final expression of the operator to be potentially used in a concrete application. In particular, fuzzy implication functions with simple expressions have low computational cost and they reduce the spreading of numerical errors.
Sebastia Massanet   +2 more
openaire   +1 more source

Attribute Implications in a Fuzzy Setting

2006
The paper is an overview of recent developments concerning attribute implications in a fuzzy setting. Attribute implications are formulas of the form A $\Longrightarrow$ B, where A and B are collections of attributes, which describe dependencies between attributes. Attribute implications are studied in several areas of computer science and mathematics.
Radim Belohlávek, Vilém Vychodil
openaire   +1 more source

Fuzzy implications based on semicopulas

Fuzzy Sets and Systems, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michal Baczynski 0001   +4 more
openaire   +1 more source

LEVELS OF SIGNIFICANCE FOR FUZZY IMPLICATION

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1994
Fuzzy implication operators have been proposed as a tool for measuring the strength of connections between recorded concepts. Hasse diagrams are used to graphically illustrate the sometimes complicated relationships between such concepts. Establishing measures of significance for fuzzy implications is the focus of this paper.
Wyllis Bandler, Susan I. Hruska
openaire   +1 more source

Implicative Fuzzy Associative Memories

IEEE Transactions on Fuzzy Systems, 2006
Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. In this paper, we introduce implicative fuzzy associative memories (IFAMs), a class of associative neural ...
Peter Sussner, Marcos Eduardo Valle
openaire   +1 more source

Fuzzy hypotheses for GUHA implications

Fuzzy Sets and Systems, 1998
Abstract This paper presents a fuzzy generalization of a sophisticated approach to exploratory data analysis, the general unary hypotheses automaton (GUHA). The GUHA paradigm, to automatically generate sentences of an observational calculus which are supported by given data, has been attracting attention for nearly 30 years.
openaire   +1 more source

Sheffer Stroke Fuzzy Implications

2017
A new family of fuzzy implications, motivated by classic Sheffer stroke operator, is introduced. Sheffer stroke, which is a negation of a conjunction and is called NAND as well, is one of the two operators that can be used by itself, without any other logical operators, to constitute a logical formal system.
Wanda Niemyska   +2 more
openaire   +1 more source

Generated fuzzy implicators and fuzzy preference structures

Kybernetika, 2012
Following the book by \textit{J. Fodor} and \textit{M. Roubens} [Fuzzy preference modelling and multicriteria decision support. Dordrecht: Kluwer Academic Publishers (1994; Zbl 0827.90002)], a fuzzy preference structure can be constructed by a so-called monotone generator triplet \((p,i,j)\) which has to fulfill some axiomatic properties. In this paper,
Vladislav Biba, Dana Hlinená
openaire   +2 more sources

Home - About - Disclaimer - Privacy