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Detail publikace
POKORNÝ, M. ŽELASKO, P. ROUPEC, J.
Originální název
Genetic Algorithm Utilization in Fuzzy Regression Modelling
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This paper introduces a soft-computing oriented approach to Takagi-Sugeno fuzzy modelling using the evolutionary principles. The presented algorithm allows determination of relevant input variables of fuzzy model from their potential candidates. Genetic algorithms are applied to optimize fuzzy input variables space through genetic fuzzy clustering procedure and to identify the fuzzy model. Some advanced procedures e.g. individuals lifetime limitation and a shade zone of genes are used. To clarify the advantages of the proposed approaches the numerical example of modellin of fuzzy non-linear system is presented.
Klíčová slova
Genetic algorithm;fuzzy model identification
Autoři
POKORNÝ, M.; ŽELASKO, P.; ROUPEC, J.
Vydáno
29. 8. 2004
Místo
Awaji, Japan
Strany od
154
Strany do
161
Strany počet
8
BibTex
@inproceedings{BUT20755, author="Miroslav {Pokorný} and Petr {Želasko} and Jan {Roupec}", title="Genetic Algorithm Utilization in Fuzzy Regression Modelling", booktitle="Proceedings of Taiwan-Japan Symposium 2004 On Fuzzy Systems & Innovational Computing", year="2004", number="1", pages="8", address="Awaji, Japan" }