Detail publikace

Genetic Algorithm Utilization in Fuzzy Regression Modelling

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"
}