Publ3 search

Z6_60GI02O0O8IDC0QEJUJ26TJDI4

Error:

Javascript is disabled in this browser. This page requires Javascript. Modify your browser's settings to allow Javascript to execute. See your browser's documentation for specific instructions.

Publikační činnost

Probíhá načítání, čekejte prosím...

Record type:	stať ve sborníku (D)
Home Department:	Ústav pro výzkum a aplikace fuzzy modelování (94410)
Title:	Genetic algorithms in fuzzy approximation
Citace	Daňková, M. a Štěpnička, M. Genetic algorithms in fuzzy approximation. In: Proc. of the Joint 4th EUSFLAT2005 and 11th LFA2005. Barcelona: Universitat Politecnica de Catalunya & EUSFLAT, 2005. Universitat Politecnica de Catalunya & EUSFLAT, 2005. s. 651-656. ISBN 84-7663-872-3.

Subtitle
Publication year:	2005
Obor:	Obecná matematika
Number of pages:	6
Page from:	651
Page to:	656
Form of publication:
ISBN code:	84-7663-872-3
ISSN code:
Proceedings title:	Proc. of the Joint 4th EUSFLAT2005 and 11th LFA2005
Proceedings:
Publisher name:	Universitat Politecnica de Catalunya & EUSFLAT
Place of publishing:	Barcelona
Country of Publication:	Sborník vydaný v zahraničí
Název konference:	Joint EUSFLAT - LFA 2005
Místo konání konference:	Barcelona, Španělosko
Datum zahájení konference:
Typ akce podle státní příslušnosti účastníků:	Celosvětová akce
WoS code:
EID:

Key words in English:	Fuzzy approximation; Additive normal form; Conjunctive normal form; Disjunctive normal form; Genetic algorithm
Annotation in original language:	Three universal approximation formulas given by so called disjunctive, conjunctive and additive normal forms are recalled. Properties and relations between all three formulas are studied. The quality of approximation is shown and the role of genetic algorithms in the field of fuzzy approximation is explained.
Annotation in english language:	Three universal approximation formulas given by so called disjunctive, conjunctive and additive normal forms are recalled. Properties and relations between all three formulas are studied. The quality of approximation is shown and the role of genetic algorithms in the field of fuzzy approximation is explained.

References

Reference

R01:

Complementary Content

${loading}