Results 161 to 170 of about 5,901 (210)

Defuzzification of fuzzy intervals

Fuzzy Sets and Systems, 1991
Studied is the problem of transformation (defuzzification) of a fuzzy set (fuzzy interval) defined in \(\mathbb{R}\) into its numerical representative. Three commonly used methods being investigated in the paper are: mean of maxima (MOM), centre-of area (COA) and fuzzy mean (FM). Consider \(A: \mathbb{R}\to[0,1]\). The MOM method selects an element in \
Rakesh Govind
exaly   +3 more sources

Alpha-cut representation used for defuzzification in rule-based systems [PDF]

Fuzzy Sets and Systems, 2020
Alpha-cut representation of fuzzy sets has been used as a basis for fuzzy numbers ranking in some applications but rarely used for defuzzification of rule-based systems or fuzzy controllers.
Jerry Mendel, Amir Pourabdollah, Robert John   +2 more
exaly   +2 more sources

On a class of defuzzification functionals

2008 International Multiconference on Computer Science and Information Technology, 2008
Classical convex fuzzy numbers have many disadvantages. The main one is that every operation on this type of fuzzy numbers induces the growing fuzziness level. Another drawback is that the arithmetic operations defined for them are not complementary, for instance: addition and subtraction. Therefore the first author (W.
Witold Kosinski, Wieslaw Piasecki
openaire   +2 more sources

Cooperative neighbors in defuzzification

Fuzzy Sets and Systems, 1996
Abstract Defuzzification is a problem of optimized selection of an element from a fuzzy set. It is in fact an optimization problem, optimization with respect to the whole system under consideration. We believe that neighbors can contribute and make a better selection in the process of defuzzification.
Shounak Roychowdhury, Bo-Hyeun Wang
openaire   +2 more sources

Type reduction operators for interval type–2 defuzzification [PDF]

Information Sciences, 2018
Fuzzy sets are an important approach to model uncertainty. Defuzzification maps fuzzy sets to non–fuzzy (crisp) values. Type–2 fuzzy sets model uncertainty in the degree of membership in a fuzzy set.
Chao Chen, Robert John, Thomas Runkler   +2 more
exaly   +2 more sources

A survey of defuzzification strategies

International Journal of Intelligent Systems, 2001
The authors revisit the defuzzification problem and they propose a few inquiries into the nature of defuzzification. The authors first give a brief survey of the main defuzzification schemes that have been proposed during the last few years. They still find that COG (center of gravity) and MOM (mean of maxima) remain standard defuzzification operators ...
Shounak Roychowdhury, Witold Pedrycz
openaire   +3 more sources

Defuzzification in Fuzzy Controllers

Journal of Intelligent & Fuzzy Systems, 1993
An important subject in fuzzy control theory is tuning of a fuzzy controller. If one wants to tune a fuzzy controller, one can focus on the choice of rules, membership functions, number of input and output fuzzy sets and their degree of overlapping, implication, and connection operations, and defuzzification method.
Hans Hellendoorn, Christoph Thomas
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Partition validity and defuzzification

Fuzzy Sets and Systems, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Antonio Flores-Sintas, José Manuel Cadenas, Fernando Martin   +2 more
openaire   +1 more source

Note on “A new approach for defuzzification”

Fuzzy Sets and Systems, 2002
The authors show by an example that Definition 3 in \textit{M. Ma, A. Kandel} and \textit{M. Friedman}'s paper [Fuzzy Sets Syst. 111, 351-356 (2000; Zbl 0968.93046)] does not give a metric.
Saeid Abbasbandy, B. Asady
openaire   +1 more source

SET DEFUZZIFICATION AND CHOQUET INTEGRAL

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2001
In this paper, we discuss the defuzzification problem. We first propose a set defuzzification method, (from a fuzzy set to a crisp set) by using the Aumann integral. From the obtained set to a point, we have two methods of defuzzification. One of these uses the mean value method and the other uses a fuzzy measure.
Yukio Ogura, Shoumei Li, Dan A. Ralescu
openaire   +2 more sources