Detail publikace

Genetic Algorithm Utilization in Fuzzy Regression Modelling

POKORNÝ, M. ŽELASKO, P. ROUPEC, J.

Anglický název

Genetic Algorithm Utilization in Fuzzy Regression Modelling

Typ

Stať ve sborníku v databázi WoS či Scopus

Jazyk

en

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 anglicky

Genetic algorithm;fuzzy model identification

Vydáno

2004-08-29

Místo

Awaji, Japan

Kniha

Proceedings of Taiwan-Japan Symposium 2004 On Fuzzy Systems & Innovational Computing

Strany od–do

154–

Počet stran

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"
}